LeetCode面试刷题之Java版本。

1.LeetCode Solutions Program Creek Version 0.0

2.Contents 1 Rotate Array in Java 7 2 Evaluate Reverse Polish Notation 9 3 Solution of Longest Palindromic Substring in Java 11 4 Solution Word Break 15 5 Word Break II 18 6 Word Ladder 20 7 Median of Two Sorted Arrays Java 23 8 Regular Expression Matching in Java 25 9 Merge Intervals 27 10 Insert Interval 29 11 Two Sum 31 12 Two Sum II Input array is sorted 32 13 Two Sum III Data structure design 33 14 3Sum 34 15 4Sum 36 16 3Sum Closest 38 17 String to Integer (atoi) 39 18 Merge Sorted Array 40 19 Valid Parentheses 42 20 Implement strStr() 43 2 | 181

3. Contents 21 Set Matrix Zeroes 44 22 Search Insert Position 46 23 Longest Consecutive Sequence Java 47 24 Valid Palindrome 49 25 Spiral Matrix 52 26 Search a 2D Matrix 55 27 Rotate Image 56 28 Triangle 58 29 Distinct Subsequences Total 60 30 Maximum Subarray 62 31 Maximum Product Subarray 63 32 Remove Duplicates from Sorted Array 64 33 Remove Duplicates from Sorted Array II 67 34 Longest Substring Without Repeating Characters 69 35 Longest Substring Which Contains 2 Unique Characters 71 36 Palindrome Partitioning 73 37 Reverse Words in a String 75 38 Find Minimum in Rotated Sorted Array 76 39 Find Minimum in Rotated Sorted Array II 77 40 Find Peak Element 78 41 Min Stack 79 42 Majority Element 80 43 Combination Sum 82 44 Best Time to Buy and Sell Stock 83 45 Best Time to Buy and Sell Stock II 84 Program Creek 3 | 181

4.Contents 46 Best Time to Buy and Sell Stock III 85 47 Best Time to Buy and Sell Stock IV 86 48 Longest Common Prefix 88 49 Largest Number 89 50 Combinations 90 51 Compare Version Numbers 92 52 Gas Station 93 53 Candy 95 54 Jump Game 96 55 Pascal’s Triangle 97 56 Container With Most Water 98 57 Count and Say 99 58 Repeated DNA Sequences 100 59 Add Two Numbers 101 60 Reorder List 105 61 Linked List Cycle 109 62 Copy List with Random Pointer 111 63 Merge Two Sorted Lists 114 64 Merge k Sorted Lists 116 65 Remove Duplicates from Sorted List 117 66 Partition List 119 67 LRU Cache 121 68 Intersection of Two Linked Lists 124 69 Java PriorityQueue Class Example 125 70 Solution for Binary Tree Preorder Traversal in Java 127 4 | 181 Program Creek

5. Contents 71 Solution of Binary Tree Inorder Traversal in Java 128 72 Solution of Iterative Binary Tree Postorder Traversal in Java 130 73 Validate Binary Search Tree 131 74 Flatten Binary Tree to Linked List 133 75 Path Sum 134 76 Construct Binary Tree from Inorder and Postorder Traversal 136 77 Convert Sorted Array to Binary Search Tree 137 78 Convert Sorted List to Binary Search Tree 138 79 Minimum Depth of Binary Tree 140 80 Binary Tree Maximum Path Sum 142 81 Balanced Binary Tree 143 82 Symmetric Tree 145 83 Clone Graph Java 146 84 How Developers Sort in Java? 149 85 Solution Merge Sort LinkedList in Java 151 86 Quicksort Array in Java 154 87 Solution Sort a linked list using insertion sort in Java 156 88 Maximum Gap 158 89 Iteration vs. Recursion in Java 160 90 Edit Distance in Java 163 91 Single Number 165 92 Single Number II 166 93 Twitter Codility Problem Max Binary Gap 166 94 Number of 1 Bits 167 95 Reverse Bits 168 Program Creek 5 | 181

6.Contents 96 Permutations 169 97 Permutations II 171 98 Permutation Sequence 173 99 Generate Parentheses 175 100 Reverse Integer 176 101 Palindrome Number 178 102 Pow(x, n) 179 6 | 181 Program Creek

7.1 Rotate Array in Java You may have been using Java for a while. Do you think a simple Java array question can be a challenge? Let’s use the following problem to test. Problem: Rotate an array of n elements to the right by k steps. For example, with n = 7 and k = 3, the array [1,2,3,4,5,6,7] is rotated to [5,6,7,1,2,3,4]. How many different ways do you know to solve this problem? 1.1 Solution 1 - Intermediate Array In a straightforward way, we can create a new array and then copy elements to the new array. Then change the original array by using System.arraycopy(). public void rotate(int[] nums, int k) { if(k > nums.length) k=k%nums.length; int[] result = new int[nums.length]; for(int i=0; i < k; i++){ result[i] = nums[nums.length-k+i]; } int j=0; for(int i=k; i<nums.length; i++){ result[i] = nums[j]; j++; } System.arraycopy( result, 0, nums, 0, nums.length ); } Space is O(n) and time is O(n). 1.2 Solution 2 - Bubble Rotate Can we do this in O(1) space? This solution is like a bubble sort. public static void rotate(int[] arr, int order) { if (arr == null || order < 0) { throw new IllegalArgumentException("Illegal argument!"); 7 | 181

8.1 Rotate Array in Java } for (int i = 0; i < order; i++) { for (int j = arr.length - 1; j > 0; j--) { int temp = arr[j]; arr[j] = arr[j - 1]; arr[j - 1] = temp; } } } However, the time is O(n*k). 1.3 Solution 3 - Reversal Can we do this in O(1) space and in O(n) time? The following solution does. Assuming we are given 1,2,3,4,5,6 and order 2. The basic idea is: 1. Divide the array two parts: 1,2,3,4 and 5, 6 2. Rotate first part: 4,3,2,1,5,6 3. Rotate second part: 4,3,2,1,6,5 4. Rotate the whole array: 5,6,1,2,3,4 public static void rotate(int[] arr, int order) { order = order % arr.length; if (arr == null || order < 0) { throw new IllegalArgumentException("Illegal argument!"); } //length of first part int a = arr.length - order; reverse(arr, 0, a-1); reverse(arr, a, arr.length-1); reverse(arr, 0, arr.length-1); } public static void reverse(int[] arr, int left, int right){ if(arr == null || arr.length == 1) return; while(left < right){ int temp = arr[left]; arr[left] = arr[right]; arr[right] = temp; left++; right--; 8 | 181 Program Creek

9. } } 2 Evaluate Reverse Polish Notation The problem: Evaluate the value of an arithmetic expression in Reverse Polish Notation. Valid operators are +, -, *, /. Each operand may be an integer or another expression. Some examples: ["2", "1", "+", "3", "*"] -> ((2 + 1) * 3) -> 9 ["4", "13", "5", "/", "+"] -> (4 + (13 / 5)) -> 6 2.1 Naive Approach This problem is simple. After understanding the problem, we should quickly realize that this problem can be solved by using a stack. We can loop through each element in the given array. When it is a number, push it to the stack. When it is an operator, pop two numbers from the stack, do the calculation, and push back the result. The following is the code. It runs great by feeding a small test. However, this code 9 | 181

10.2 Evaluate Reverse Polish Notation contains compilation errors in leetcode. Why? public class Test { public static void main(String[] args) throws IOException { String[] tokens = new String[] { "2", "1", "+", "3", "*" }; System.out.println(evalRPN(tokens)); } public static int evalRPN(String[] tokens) { int returnValue = 0; String operators = "+-*/"; Stack<String> stack = new Stack<String>(); for (String t : tokens) { if (!operators.contains(t)) { stack.push(t); } else { int a = Integer.valueOf(stack.pop()); int b = Integer.valueOf(stack.pop()); switch (t) { case "+": stack.push(String.valueOf(a + b)); break; case "-": stack.push(String.valueOf(b - a)); break; case "*": stack.push(String.valueOf(a * b)); break; case "/": stack.push(String.valueOf(b / a)); break; } } } returnValue = Integer.valueOf(stack.pop()); return returnValue; } } The problem is that switch string statement is only available from JDK 1.7. Leetcode apparently use versions below that. 10 | 181 Program Creek

11.2.2 Accepted Solution If you want to use switch statement, you can convert the above by using the following code which use the index of a string "+-*/". public class Solution { public int evalRPN(String[] tokens) { int returnValue = 0; String operators = "+-*/"; Stack<String> stack = new Stack<String>(); for(String t : tokens){ if(!operators.contains(t)){ stack.push(t); }else{ int a = Integer.valueOf(stack.pop()); int b = Integer.valueOf(stack.pop()); int index = operators.indexOf(t); switch(index){ case 0: stack.push(String.valueOf(a+b)); break; case 1: stack.push(String.valueOf(b-a)); break; case 2: stack.push(String.valueOf(a*b)); break; case 3: stack.push(String.valueOf(b/a)); break; } } } returnValue = Integer.valueOf(stack.pop()); return returnValue; } } 11 | 181

12.3 Solution of Longest Palindromic Substring in Java 3 Solution of Longest Palindromic Substring in Java Finding the longest palindromic substring is a classic problem of coding interview. In this post, I will summarize 3 different solutions for this problem. 3.1 Naive Approach Naively, we can simply examine every substring and check if it is palindromic. The time complexity is O(nˆ3). If this is submitted to LeetCode onlinejudge, an error mes- sage will be returned - "Time Limit Exceeded". Therefore, this approach is just a start, we need a better algorithm. public static String longestPalindrome1(String s) { int maxPalinLength = 0; String longestPalindrome = null; int length = s.length(); // check all possible sub strings for (int i = 0; i < length; i++) { for (int j = i + 1; j < length; j++) { int len = j - i; String curr = s.substring(i, j + 1); if (isPalindrome(curr)) { if (len > maxPalinLength) { longestPalindrome = curr; maxPalinLength = len; } } } } return longestPalindrome; } public static boolean isPalindrome(String s) { for (int i = 0; i < s.length() - 1; i++) { if (s.charAt(i) != s.charAt(s.length() - 1 - i)) { return false; } } return true; } 12 | 181 Program Creek

13. 3 Solution of Longest Palindromic Substring in Java 3.2 Dynamic Programming Let s be the input string, i and j are two indices of the string. Define a 2-dimension array "table" and let table[i][j] denote whether substring from i to j is palindrome. Start condition: table[i][i] == 1; table[i][i+1] == 1 => s.charAt(i) == s.charAt(i+1) Changing condition: table[i+1][j-1] == 1 && s.charAt(i) == s.charAt(j) => table[i][j] == 1 Time O(nˆ2) Space O(nˆ2) public static String longestPalindrome2(String s) { if (s == null) return null; if(s.length() <=1) return s; int maxLen = 0; String longestStr = null; int length = s.length(); int[][] table = new int[length][length]; //every single letter is palindrome for (int i = 0; i < length; i++) { table[i][i] = 1; } printTable(table); //e.g. bcba //two consecutive same letters are palindrome for (int i = 0; i <= length - 2; i++) { if (s.charAt(i) == s.charAt(i + 1)){ table[i][i + 1] = 1; longestStr = s.substring(i, i + 2); } } printTable(table); //condition for calculate whole table for (int l = 3; l <= length; l++) { for (int i = 0; i <= length-l; i++) { Program Creek 13 | 181

14.3 Solution of Longest Palindromic Substring in Java int j = i + l - 1; if (s.charAt(i) == s.charAt(j)) { table[i][j] = table[i + 1][j - 1]; if (table[i][j] == 1 && l > maxLen) longestStr = s.substring(i, j + 1); } else { table[i][j] = 0; } printTable(table); } } return longestStr; } public static void printTable(int[][] x){ for(int [] y : x){ for(int z: y){ System.out.print(z + " "); } System.out.println(); } System.out.println("------"); } Given an input, we can use printTable method to examine the table after each itera- tion. For example, if input string is "dabcba", the final matrix would be the following: 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 From the table, we can clear see that the longest string is in cell table[1][5]. 3.3 Simple Algorithm From Yifan’s comment below. Time O(nˆ2), Space O(1) public String longestPalindrome(String s) { if (s.isEmpty()) { return null; } if (s.length() == 1) { return s; } 14 | 181 Program Creek

15. String longest = s.substring(0, 1); for (int i = 0; i < s.length(); i++) { // get longest palindrome with center of i String tmp = helper(s, i, i); if (tmp.length() > longest.length()) { longest = tmp; } // get longest palindrome with center of i, i+1 tmp = helper(s, i, i + 1); if (tmp.length() > longest.length()) { longest = tmp; } } return longest; } // Given a center, either one letter or two letter, // Find longest palindrome public String helper(String s, int begin, int end) { while (begin >= 0 && end <= s.length() - 1 && s.charAt(begin) == s.charAt(end)) { begin--; end++; } return s.substring(begin + 1, end); } 3.4 Manacher’s Algorithm Manacher’s algorithm is much more complicated to figure out, even though it will bring benefit of time complexity of O(n). Since it is not typical, there is no need to waste time on that. 4 Solution Word Break Given a string s and a dictionary of words dict, determine if s can be segmented into a space-separated sequence of one or more dictionary words. For example, given s = "leetcode", dict = ["leet", "code"]. Return true because "leetcode" can be segmented as "leet code". 15 | 181

16.4 Solution Word Break 4.1 Naive Approach This problem can be solve by using a naive approach, which is trivial. A discussion can always start from that though. public class Solution { public boolean wordBreak(String s, Set<String> dict) { return wordBreakHelper(s, dict, 0); } public boolean wordBreakHelper(String s, Set<String> dict, int start){ if(start == s.length()) return true; for(String a: dict){ int len = a.length(); int end = start+len; //end index should be <= string length if(end > s.length()) continue; if(s.substring(start, start+len).equals(a)) if(wordBreakHelper(s, dict, start+len)) return true; } return false; } } Time: O(nˆ2) This solution exceeds the time limit. 4.2 Dynamic Programming The key to solve this problem by using dynamic programming approach: • Define an array t[] such that t[i]==true =>0-(i-1) can be segmented using dictio- nary • Initial state t[0] == true public class Solution { public boolean wordBreak(String s, Set<String> dict) { boolean[] t = new boolean[s.length()+1]; t[0] = true; //set first to be true, why? //Because we need initial state for(int i=0; i<s.length(); i++){ 16 | 181 Program Creek

17. 4 Solution Word Break //should continue from match position if(!t[i]) continue; for(String a: dict){ int len = a.length(); int end = i + len; if(end > s.length()) continue; if(t[end]) continue; if(s.substring(i, end).equals(a)){ t[end] = true; } } } return t[s.length()]; } } Time: O(string length * dict size) One tricky part of this solution is the case: INPUT: "programcreek", ["programcree","program","creek"]. We should get all possible matches, not stop at "programcree". 4.3 Regular Expression The problem is supposed to be equivalent to matching the regexp (leet|code)*, which means that it can be solved by building a DFA in O(2m) ˆ and executing it in O(n). (Thanks to hdante.) Leetcode online judge does not allow using Pattern class though. public static void main(String[] args) { HashSet<String> dict = new HashSet<String>(); dict.add("go"); dict.add("goal"); dict.add("goals"); dict.add("special"); StringBuilder sb = new StringBuilder(); for(String s: dict){ sb.append(s + "|"); } String pattern = sb.toString().substring(0, sb.length()-1); Program Creek 17 | 181

18. pattern = "("+pattern+")*"; Pattern p = Pattern.compile(pattern); Matcher m = p.matcher("goalspecial"); if(m.matches()){ System.out.println("match"); } } 4.4 The More Interesting Problem The dynamic solution can tell us whether the string can be broken to words, but can not tell us what words the string is broken to. So how to get those words? Check out Word Break II. 5 Word Break II Given a string s and a dictionary of words dict, add spaces in s to construct a sentence where each word is a valid dictionary word. Return all such possible sentences. For example, given s = "catsanddog", dict = ["cat", "cats", "and", "sand", "dog"], the solution is ["cats and dog", "cat sand dog"]. 5.1 Java Solution - Dynamic Programming This problem is very similar to Word Break. Instead of using a boolean array to track the match positions, we need to track the actual words. Then we can use depth first search to get all the possible paths, i.e., the list of strings. The following diagram shows the structure of the tracking array. 18 | 181

19. 5 Word Break II public static List<String> wordBreak(String s, Set<String> dict) { //create an array of ArrayList<String> List<String> dp[] = new ArrayList[s.length()+1]; dp[0] = new ArrayList<String>(); for(int i=0; i<s.length(); i++){ if( dp[i] == null ) continue; for(String word:dict){ int len = word.length(); int end = i+len; if(end > s.length()) continue; if(s.substring(i,end).equals(word)){ if(dp[end] == null){ dp[end] = new ArrayList<String>(); } dp[end].add(word); } } } List<String> result = new LinkedList<String>(); if(dp[s.length()] == null) return result; Program Creek 19 | 181

20. ArrayList<String> temp = new ArrayList<String>(); dfs(dp, s.length(), result, temp); return result; } public static void dfs(List<String> dp[],int end,List<String> result, ArrayList<String> tmp){ if(end <= 0){ String path = tmp.get(tmp.size()-1); for(int i=tmp.size()-2; i>=0; i--){ path += " " + tmp.get(i) ; } result.add(path); return; } for(String str : dp[end]){ tmp.add(str); dfs(dp, end-str.length(), result, tmp); tmp.remove(tmp.size()-1); } } 6 Word Ladder The problem: Given two words (start and end), and a dictionary, find the length of shortest trans- formation sequence from start to end, such that: Only one letter can be changed at a time Each intermediate word must exist in the dictionary For example, Given: start = "hit" end = "cog" dict = ["hot","dot","dog","lot","log"] As one shortest transformation is "hit" ->"hot" ->"dot" ->"dog" ->"cog", the program should return its length 5. Note: Return 0 if there is no such transformation sequence. All words have the same length. All words contain only lowercase alphabetic characters. This problem is a classic problem that has been asked frequently during interviews. 20 | 181

21. 6 Word Ladder The following are two Java solutions. 6.1 Naive Approach In a simplest way, we can start from start word, change one character each time, if it is in the dictionary, we continue with the replaced word, until start == end. public class Solution { public int ladderLength(String start, String end, HashSet<String> dict) { int len=0; HashSet<String> visited = new HashSet<String>(); for(int i=0; i<start.length(); i++){ char[] startArr = start.toCharArray(); for(char c=’a’; c<=’z’; c++){ if(c==start.toCharArray()[i]){ continue; } startArr[i] = c; String temp = new String(startArr); if(dict.contains(temp)){ len++; start = temp; if(temp.equals(end)){ return len; } } } } return len; } } Apparently, this is not good enough. The following example exactly shows the problem. It can not find optimal path. The output is 3, but it actually only takes 2. Input: "a", "c", ["a","b","c"] Output: 3 Expected: 2 6.2 Breath First Search So we quickly realize that this looks like a tree searching problem for which breath first guarantees the optimal solution. Program Creek 21 | 181

22.6 Word Ladder Assuming we have some words in the dictionary, and the start is "hit" as shown in the diagram below. We can use two queues to traverse the tree, one stores the nodes, the other stores the step numbers. Updated on 2/27/2015. public int ladderLength(String start, String end, HashSet<String> dict) { if (dict.size() == 0) return 0; dict.add(end); LinkedList<String> wordQueue = new LinkedList<String>(); LinkedList<Integer> distanceQueue = new LinkedList<Integer>(); wordQueue.add(start); distanceQueue.add(1); //track the shortest path int result = Integer.MAX_VALUE; while (!wordQueue.isEmpty()) { String currWord = wordQueue.pop(); Integer currDistance = distanceQueue.pop(); if (currWord.equals(end)) { result = Math.min(result, currDistance); } for (int i = 0; i < currWord.length(); i++) { char[] currCharArr = currWord.toCharArray(); for (char c = ’a’; c <= ’z’; c++) { 22 | 181 Program Creek

23. currCharArr[i] = c; String newWord = new String(currCharArr); if (dict.contains(newWord)) { wordQueue.add(newWord); distanceQueue.add(currDistance + 1); dict.remove(newWord); } } } } if (result < Integer.MAX_VALUE) return result; else return 0; } 6.3 What learned from this problem? • Use breath-first or depth-first search to solve problems • Use two queues, one for words and another for counting 7 Median of Two Sorted Arrays Java LeetCode Problem: There are two sorted arrays A and B of size m and n respectively. Find the median of the two sorted arrays. The overall run time complexity should be O(log (m+n)). 7.1 Java Solution This problem can be converted to the problem of finding kth element, k is (A’s length + B’ Length)/2. If any of the two arrays is empty, then the kth element is the non-empty array’s kth element. If k == 0, the kth element is the first element of A or B. For normal cases(all other cases), we need to move the pointer at the pace of half of an array length. public static double findMedianSortedArrays(int A[], int B[]) { int m = A.length; int n = B.length; 23 | 181

24. if ((m + n) % 2 != 0) // odd return (double) findKth(A, B, (m + n) / 2, 0, m - 1, 0, n - 1); else { // even return (findKth(A, B, (m + n) / 2, 0, m - 1, 0, n - 1) + findKth(A, B, (m + n) / 2 - 1, 0, m - 1, 0, n - 1)) * 0.5; } } public static int findKth(int A[], int B[], int k, int aStart, int aEnd, int bStart, int bEnd) { int aLen = aEnd - aStart + 1; int bLen = bEnd - bStart + 1; // Handle special cases if (aLen == 0) return B[bStart + k]; if (bLen == 0) return A[aStart + k]; if (k == 0) return A[aStart] < B[bStart] ? A[aStart] : B[bStart]; int aMid = aLen * k / (aLen + bLen); // a’s middle count int bMid = k - aMid - 1; // b’s middle count // make aMid and bMid to be array index aMid = aMid + aStart; bMid = bMid + bStart; if (A[aMid] > B[bMid]) { k = k - (bMid - bStart + 1); aEnd = aMid; bStart = bMid + 1; } else { k = k - (aMid - aStart + 1); bEnd = bMid; aStart = aMid + 1; } return findKth(A, B, k, aStart, aEnd, bStart, bEnd); } 7.2 The Steps of the Algorithm Thanks to Gunner86. The description of the algorithm is awesome! 1) Calculate the medians m1 and m2 of the input arrays ar1[] and ar2[] respectively. 2) If m1 and m2 both are equal then we are done, and return m1 (or m2) 3) If m1 24 | 181

25. 8 Regular Expression Matching in Java is greater than m2, then median is present in one of the below two subarrays. a) From first element of ar1 to m1 (ar1[0...|_n/2_|]) b) From m2 to last element of ar2 (ar2[|_n/2_|...n-1]) 4) If m2 is greater than m1, then median is present in one of the below two subarrays. a) From m1 to last element of ar1 (ar1[|_n/2_|...n-1]) b) From first element of ar2 to m2 (ar2[0...|_n/2_|]) 5) Repeat the above process until size of both the subarrays becomes 2. 6) If size of the two arrays is 2 then use below formula to get the median. Median = (max(ar1[0], ar2[0]) + min(ar1[1], ar2[1]))/2 8 Regular Expression Matching in Java Problem: Implement regular expression matching with support for ’.’ and ’*’. ’.’ Matches any single character. ’*’ Matches zero or more of the preceding element. The matching should cover the entire input string (not partial). The function prototype should be: bool isMatch(const char *s, const char *p) Some examples: isMatch("aa","a") return false isMatch("aa","aa") return true isMatch("aaa","aa") return false isMatch("aa", "a*") return true isMatch("aa", ".*") return true isMatch("ab", ".*") return true isMatch("aab", "c*a*b") return true 8.1 Analysis First of all, this is one of the most difficulty problems. It is hard to handle many cases. The problem should be simplified to handle 2 basic cases: • the second char of pattern is "*" • the second char of pattern is not "*" For the 1st case, if the first char of pattern is not ".", the first char of pattern and string should be the same. Then continue to match the left part. For the 2nd case, if the first char of pattern is "." or first char of pattern == the first i char of string, continue to match the left. Be careful about the offset. Program Creek 25 | 181

26.8 Regular Expression Matching in Java 8.2 Java Solution 1 (Short) The following Java solution is accepted. public class Solution { public boolean isMatch(String s, String p) { if(p.length() == 0) return s.length() == 0; //p’s length 1 is special case if(p.length() == 1 || p.charAt(1) != ’*’){ if(s.length() < 1 || (p.charAt(0) != ’.’ && s.charAt(0) != p.charAt(0))) return false; return isMatch(s.substring(1), p.substring(1)); }else{ int len = s.length(); int i = -1; while(i<len && (i < 0 || p.charAt(0) == ’.’ || p.charAt(0) == s.charAt(i))){ if(isMatch(s.substring(i+1), p.substring(2))) return true; i++; } return false; } } } 8.3 Java Solution 2 (More Readable) public boolean isMatch(String s, String p) { // base case if (p.length() == 0) { return s.length() == 0; } // special case if (p.length() == 1) { // if the length of s is 0, return false if (s.length() < 1) { return false; } 26 | 181 Program Creek

27. //if the first does not match, return false else if ((p.charAt(0) != s.charAt(0)) && (p.charAt(0) != ’.’)) { return false; } // otherwise, compare the rest of the string of s and p. else { return isMatch(s.substring(1), p.substring(1)); } } // case 1: when the second char of p is not ’*’ if (p.charAt(1) != ’*’) { if (s.length() < 1) { return false; } if ((p.charAt(0) != s.charAt(0)) && (p.charAt(0) != ’.’)) { return false; } else { return isMatch(s.substring(1), p.substring(1)); } } // case 2: when the second char of p is ’*’, complex case. else { //case 2.1: a char & ’*’ can stand for 0 element if (isMatch(s, p.substring(2))) { return true; } //case 2.2: a char & ’*’ can stand for 1 or more preceding element, //so try every sub string int i = 0; while (i<s.length() && (s.charAt(i)==p.charAt(0) || p.charAt(0)==’.’)){ if (isMatch(s.substring(i + 1), p.substring(2))) { return true; } i++; } return false; } } 27 | 181

28.9 Merge Intervals 9 Merge Intervals Problem: Given a collection of intervals, merge all overlapping intervals. For example, Given [1,3],[2,6],[8,10],[15,18], return [1,6],[8,10],[15,18]. 9.1 Thoughts of This Problem The key to solve this problem is defining a Comparator first to sort the arraylist of Intevals. And then merge some intervals. The take-away message from this problem is utilizing the advantage of sorted list/ar- ray. 9.2 Java Solution class Interval { int start; int end; Interval() { start = 0; end = 0; } Interval(int s, int e) { start = s; end = e; } } public class Solution { public ArrayList<Interval> merge(ArrayList<Interval> intervals) { if (intervals == null || intervals.size() <= 1) return intervals; // sort intervals by using self-defined Comparator Collections.sort(intervals, new IntervalComparator()); ArrayList<Interval> result = new ArrayList<Interval>(); Interval prev = intervals.get(0); 28 | 181 Program Creek

29. for (int i = 1; i < intervals.size(); i++) { Interval curr = intervals.get(i); if (prev.end >= curr.start) { // merged case Interval merged = new Interval(prev.start, Math.max(prev.end, curr.end)); prev = merged; } else { result.add(prev); prev = curr; } } result.add(prev); return result; } } class IntervalComparator implements Comparator<Interval> { public int compare(Interval i1, Interval i2) { return i1.start - i2.start; } } 10 Insert Interval Problem: Given a set of non-overlapping & sorted intervals, insert a new interval into the intervals (merge if necessary). Example 1: Given intervals [1,3],[6,9], insert and merge [2,5] in as [1,5],[6,9]. Example 2: Given [1,2],[3,5],[6,7],[8,10],[12,16], insert and merge [4,9] in as [1,2],[3,10],[12,16]. This is because the new interval [4,9] overlaps with [3,5],[6,7],[8,10]. 29 | 181

30.10 Insert Interval 10.1 Thoughts of This Problem Quickly summarize 3 cases. Whenever there is intersection, created a new interval. 10.2 Java Solution /** * Definition for an interval. * public class Interval { * int start; * int end; * Interval() { start = 0; end = 0; } * Interval(int s, int e) { start = s; end = e; } * } */ public class Solution { public ArrayList<Interval> insert(ArrayList<Interval> intervals, Interval newInterval) { ArrayList<Interval> result = new ArrayList<Interval>(); for(Interval interval: intervals){ if(interval.end < newInterval.start){ result.add(interval); }else if(interval.start > newInterval.end){ result.add(newInterval); newInterval = interval; }else if(interval.end >= newInterval.start || interval.start <= newInterval.end){ 30 | 181 Program Creek

31. newInterval = new Interval(Math.min(interval.start, newInterval.start), Math.max(newInterval.end, interval.end)); } } result.add(newInterval); return result; } } 11 Two Sum Given an array of integers, find two numbers such that they add up to a specific target number. The function twoSum should return indices of the two numbers such that they add up to the target, where index1 must be less than index2. Please note that your returned answers (both index1 and index2) are not zero-based. For example: Input: numbers={2, 7, 11, 15}, target=9 Output: index1=1, index2=2 11.1 Naive Approach This problem is pretty straightforward. We can simply examine every possible pair of numbers in this integer array. Time complexity in worst case: O(nˆ2). public static int[] twoSum(int[] numbers, int target) { int[] ret = new int[2]; for (int i = 0; i < numbers.length; i++) { for (int j = i + 1; j < numbers.length; j++) { if (numbers[i] + numbers[j] == target) { ret[0] = i + 1; ret[1] = j + 1; } } } return ret; } 31 | 181

32. Can we do better? 11.2 Better Solution Use HashMap to store the target value. public class Solution { public int[] twoSum(int[] numbers, int target) { HashMap<Integer, Integer> map = new HashMap<Integer, Integer>(); int[] result = new int[2]; for (int i = 0; i < numbers.length; i++) { if (map.containsKey(numbers[i])) { int index = map.get(numbers[i]); result[0] = index+1 ; result[1] = i+1; break; } else { map.put(target - numbers[i], i); } } return result; } } Time complexity depends on the put and get operations of HashMap which is nor- mally O(1). Time complexity of this solution: O(n). 12 Two Sum II Input array is sorted This problem is similar to Two Sum. To solve this problem, we can use two points to scan the array from both sides. See Java solution below: public int[] twoSum(int[] numbers, int target) { if (numbers == null || numbers.length == 0) return null; int i = 0; int j = numbers.length - 1; while (i < j) { int x = numbers[i] + numbers[j]; 32 | 181

33. if (x < target) { ++i; } else if (x > target) { j--; } else { return new int[] { i + 1, j + 1 }; } } return null; } 13 Two Sum III Data structure design Design and implement a TwoSum class. It should support the following operations: add and find. add - Add the number to an internal data structure. find - Find if there exists any pair of numbers which sum is equal to the value. For example, add(1); add(3); add(5); find(4) -> true find(7) -> false 13.1 Java Solution Since the desired class need add and get operations, HashMap is a good option for this purpose. public class TwoSum { private HashMap<Integer, Integer> elements = new HashMap<Integer, Integer>(); public void add(int number) { if (elements.containsKey(number)) { elements.put(number, elements.get(number) + 1); } else { elements.put(number, 1); } } 33 | 181

34. public boolean find(int value) { for (Integer i : elements.keySet()) { int target = value - i; if (elements.containsKey(target)) { if (i == target && elements.get(target) < 2) { continue; } return true; } } return false; } } 14 3Sum Problem: Given an array S of n integers, are there elements a, b, c in S such that a + b + c = 0? Find all unique triplets in the array which gives the sum of zero. Note: Elements in a triplet (a,b,c) must be in non-descending order. (ie, a ≤ b ≤ c) The solution set must not contain duplicate triplets. For example, given array S = {-1 0 1 2 -1 -4}, A solution set is: (-1, 0, 1) (-1, -1, 2) 14.1 Naive Solution Naive solution is 3 loops, and this gives time complexity O(nˆ3). Apparently this is not an acceptable solution, but a discussion can start from here. public class Solution { public ArrayList<ArrayList<Integer>> threeSum(int[] num) { //sort array Arrays.sort(num); ArrayList<ArrayList<Integer>> result = new ArrayList<ArrayList<Integer>>(); ArrayList<Integer> each = new ArrayList<Integer>(); 34 | 181

35. 14 3Sum for(int i=0; i<num.length; i++){ if(num[i] > 0) break; for(int j=i+1; j<num.length; j++){ if(num[i] + num[j] > 0 && num[j] > 0) break; for(int k=j+1; k<num.length; k++){ if(num[i] + num[j] + num[k] == 0) { each.add(num[i]); each.add(num[j]); each.add(num[k]); result.add(each); each.clear(); } } } } return result; } } * The solution also does not handle duplicates. Therefore, it is not only time ineffi- cient, but also incorrect. Result: Submission Result: Output Limit Exceeded 14.2 Better Solution A better solution is using two pointers instead of one. This makes time complexity of O(nˆ2). To avoid duplicate, we can take advantage of sorted arrays, i.e., move pointers by >1 to use same element only once. public ArrayList<ArrayList<Integer>> threeSum(int[] num) { ArrayList<ArrayList<Integer>> result = new ArrayList<ArrayList<Integer>>(); if (num.length < 3) return result; // sort array Arrays.sort(num); for (int i = 0; i < num.length - 2; i++) { // //avoid duplicate solutions if (i == 0 || num[i] > num[i - 1]) { Program Creek 35 | 181

36. int negate = -num[i]; int start = i + 1; int end = num.length - 1; while (start < end) { //case 1 if (num[start] + num[end] == negate) { ArrayList<Integer> temp = new ArrayList<Integer>(); temp.add(num[i]); temp.add(num[start]); temp.add(num[end]); result.add(temp); start++; end--; //avoid duplicate solutions while (start < end && num[end] == num[end + 1]) end--; while (start < end && num[start] == num[start - 1]) start++; //case 2 } else if (num[start] + num[end] < negate) { start++; //case 3 } else { end--; } } } } return result; } 15 4Sum Given an array S of n integers, are there elements a, b, c, and d in S such that a + b + c + d = target? Find all unique quadruplets in the array which gives the sum of target. Note: Elements in a quadruplet (a,b,c,d) must be in non-descending order. (ie, a ≤ b ≤ c ≤ d) The solution set must not contain duplicate quadruplets. 36 | 181

37. 15 4Sum For example, given array S = {1 0 -1 0 -2 2}, and target = 0. A solution set is: (-1, 0, 0, 1) (-2, -1, 1, 2) (-2, 0, 0, 2) 15.1 Thoughts A typical k-sum problem. Time is N to the poser of (k-1). 15.2 Java Solution public ArrayList<ArrayList<Integer>> fourSum(int[] num, int target) { Arrays.sort(num); HashSet<ArrayList<Integer>> hashSet = new HashSet<ArrayList<Integer>>(); ArrayList<ArrayList<Integer>> result = new ArrayList<ArrayList<Integer>>(); for (int i = 0; i < num.length; i++) { for (int j = i + 1; j < num.length; j++) { int k = j + 1; int l = num.length - 1; while (k < l) { int sum = num[i] + num[j] + num[k] + num[l]; if (sum > target) { l--; } else if (sum < target) { k++; } else if (sum == target) { ArrayList<Integer> temp = new ArrayList<Integer>(); temp.add(num[i]); temp.add(num[j]); temp.add(num[k]); temp.add(num[l]); if (!hashSet.contains(temp)) { hashSet.add(temp); result.add(temp); } k++; l--; } Program Creek 37 | 181

38. } } } return result; } Here is the hashCode method of ArrayList. It makes sure that if all elements of two lists are the same, then the hash code of the two lists will be the same. Since each element in the ArrayList is Integer, same integer has same hash code. int hashCode = 1; Iterator<E> i = list.iterator(); while (i.hasNext()) { E obj = i.next(); hashCode = 31*hashCode + (obj==null ? 0 : obj.hashCode()); } 16 3Sum Closest Given an array S of n integers, find three integers in S such that the sum is closest to a given number, target. Return the sum of the three integers. You may assume that each input would have exactly one solution. For example, given array S = -1 2 1 -4, and target = 1. The sum that is closest to the target is 2. (-1 + 2 + 1 = 2). 16.1 Thoughts This problem is similar with 2 Sum. This kind of problem can be solve by using similar approach, i.e., two pointers from both left and right. 16.2 Java Solution public class Solution { public int threeSumClosest(int[] num, int target) { int min = Integer.MAX_VALUE; int result = 0; Arrays.sort(num); for (int i = 0; i < num.length; i++) { int j = i + 1; 38 | 181

39. int k = num.length - 1; while (j < k) { int sum = num[i] + num[j] + num[k]; int diff = Math.abs(sum - target); if(diff == 0) return 0; if (diff < min) { min = diff; result = sum; } if (sum <= target) { j++; } else { k--; } } } return result; } } Time Complexity is O(nˆ2). 17 String to Integer (atoi) Problem: Implement atoi to convert a string to an integer. Hint: Carefully consider all possible input cases. If you want a challenge, please do not see below and ask yourself what are the possible input cases. Notes: It is intended for this problem to be specified vaguely (ie, no given input specs). You are responsible to gather all the input requirements up front. 17.1 Thoughts for This Problem The vague description give us space to consider different cases. 1. null or empty string 2. white spaces 3. +/- sign 4. calculate real value 5. handle min & max 39 | 181

40.17.2 Java Solution public int atoi(String str) { if (str == null || str.length() < 1) return 0; // trim white spaces str = str.trim(); char flag = ’+’; // check negative or positive int i = 0; if (str.charAt(0) == ’-’) { flag = ’-’; i++; } else if (str.charAt(0) == ’+’) { i++; } // use double to store result double result = 0; // calculate value while (str.length() > i && str.charAt(i) >= ’0’ && str.charAt(i) <= ’9’) { result = result * 10 + (str.charAt(i) - ’0’); i++; } if (flag == ’-’) result = -result; // handle max and min if (result > Integer.MAX_VALUE) return Integer.MAX_VALUE; if (result < Integer.MIN_VALUE) return Integer.MIN_VALUE; return (int) result; } Thanks to the comment below. The solution above passes LeetCode online judge, but it haven’t considered other characters. I will update this later. 40 | 181

41. 18 Merge Sorted Array 18 Merge Sorted Array Problem: Given two sorted integer arrays A and B, merge B into A as one sorted array. Note: You may assume that A has enough space to hold additional elements from B. The number of elements initialized in A and B are m and n respectively. 18.1 Thoughts for This Problem The key to solve this problem is moving element of A and B backwards. If B has some elements left after A is done, also need to handle that case. The takeaway message from this problem is that the loop condition. This kind of condition is also used for merging two sorted linked list. 18.2 Java Solution 1 public class Solution { public void merge(int A[], int m, int B[], int n) { while(m > 0 && n > 0){ if(A[m-1] > B[n-1]){ A[m+n-1] = A[m-1]; m--; }else{ A[m+n-1] = B[n-1]; n--; } } while(n > 0){ A[m+n-1] = B[n-1]; n--; } } } 18.3 Java Solution 2 The loop condition also can use m+n like the following. public void merge(int A[], int m, int B[], int n) { int i = m - 1; int j = n - 1; int k = m + n - 1; Program Creek 41 | 181

42. while (k >= 0) { if (j < 0 || (i >= 0 && A[i] > B[j])) A[k--] = A[i--]; else A[k--] = B[j--]; } } 19 Valid Parentheses Problem: Given a string containing just the characters ’(’, ’)’, ’’, ’’, ’[’ and ’]’, determine if the input string is valid. The brackets must close in the correct order, "()" and "()[]" are all valid but "(]" and "([)]" are not. 19.1 Thoughts about This Problem Character is not a frequently used class, so need to know how to use it. 19.2 Java Solution public static boolean isValid(String s) { HashMap<Character, Character> map = new HashMap<Character, Character>(); map.put(’(’, ’)’); map.put(’[’, ’]’); map.put(’{’, ’}’); Stack<Character> stack = new Stack<Character>(); for (int i = 0; i < s.length(); i++) { char curr = s.charAt(i); if (map.keySet().contains(curr)) { stack.push(curr); } else if (map.values().contains(curr)) { if (!stack.empty() && map.get(stack.peek()) == curr) { stack.pop(); } else { return false; } } 42 | 181

43. } return stack.empty(); } 19.3 Simplified Java Solution Almost identical, but convert string to char array at the beginning. public static boolean isValid(String s) { char[] charArray = s.toCharArray(); HashMap<Character, Character> map = new HashMap<Character, Character>(); map.put(’(’, ’)’); map.put(’[’, ’]’); map.put(’{’, ’}’); Stack<Character> stack = new Stack<Character>(); for (Character c : charArray) { if (map.keySet().contains(c)) { stack.push(c); } else if (map.values().contains(c)) { if (!stack.isEmpty() && map.get(stack.peek()) == c) { stack.pop(); } else { return false; } } } return stack.isEmpty(); } 20 Implement strStr() Problem: Implement strStr(). Returns a pointer to the first occurrence of needle in haystack, or null if needle is not part of haystack. 43 | 181

44.20.1 Thoughts First, need to understand the problem correctly, the pointer simply means a sub string. Second, make sure the loop does not exceed the boundaries of two strings. 20.2 Java Solution public String strStr(String haystack, String needle) { int needleLen = needle.length(); int haystackLen = haystack.length(); if (needleLen == haystackLen && needleLen == 0) return ""; if (needleLen == 0) return haystack; for (int i = 0; i < haystackLen; i++) { // make sure in boundary of needle if (haystackLen - i + 1 < needleLen) return null; int k = i; int j = 0; while (j < needleLen && k < haystackLen && needle.charAt(j) == haystack.charAt(k)) { j++; k++; if (j == needleLen) return haystack.substring(i); } } return null; } From Tia: You have to check if a String == null before call length(), otherwise it will throw Null- PointerException. 44 | 181

45. 21 Set Matrix Zeroes 21 Set Matrix Zeroes Given a m x n matrix, if an element is 0, set its entire row and column to 0. Do it in place. 21.1 Thoughts about This Problem This problem can solve by following 4 steps: • check if first row and column are zero or not • mark zeros on first row and column • use mark to set elements • set first column and row by using marks in step 1 21.2 Java Solution public class Solution { public void setZeroes(int[][] matrix) { boolean firstRowZero = false; boolean firstColumnZero = false; //set first row and column zero or not for(int i=0; i<matrix.length; i++){ if(matrix[i][0] == 0){ firstColumnZero = true; break; } } for(int i=0; i<matrix[0].length; i++){ if(matrix[0][i] == 0){ firstRowZero = true; break; } } //mark zeros on first row and column for(int i=1; i<matrix.length; i++){ for(int j=1; j<matrix[0].length; j++){ if(matrix[i][j] == 0){ matrix[i][0] = 0; matrix[0][j] = 0; } } } Program Creek 45 | 181

46. //use mark to set elements for(int i=1; i<matrix.length; i++){ for(int j=1; j<matrix[0].length; j++){ if(matrix[i][0] == 0 || matrix[0][j] == 0){ matrix[i][j] = 0; } } } //set first column and row if(firstColumnZero){ for(int i=0; i<matrix.length; i++) matrix[i][0] = 0; } if(firstRowZero){ for(int i=0; i<matrix[0].length; i++) matrix[0][i] = 0; } } } 22 Search Insert Position Given a sorted array and a target value, return the index if the target is found. If not, return the index where it would be if it were inserted in order. You may assume no duplicates in the array. Here are few examples. [1,3,5,6], 5 -> 2 [1,3,5,6], 2 -> 1 [1,3,5,6], 7 -> 4 [1,3,5,6], 0 -> 0 22.1 Solution 1 Naively, we can just iterate the array and compare target with ith and (i+1)th element. Time complexity is O(n) public class Solution { public int searchInsert(int[] A, int target) { 46 | 181

47. if(A==null) return 0; if(target <= A[0]) return 0; for(int i=0; i<A.length-1; i++){ if(target > A[i] && target <= A[i+1]){ return i+1; } } return A.length; } } 22.2 Solution 2 This also looks like a binary search problem. We should try to make the complexity to be O(nlogn). public class Solution { public int searchInsert(int[] A, int target) { if(A==null||A.length==0) return 0; return searchInsert(A,target,0,A.length-1); } public int searchInsert(int[] A, int target, int start, int end){ int mid=(start+end)/2; if(target==A[mid]) return mid; else if(target<A[mid]) return start<mid?searchInsert(A,target,start,mid-1):start; else return end>mid?searchInsert(A,target,mid+1,end):(end+1); } } 47 | 181

48.23 Longest Consecutive Sequence Java 23 Longest Consecutive Sequence Java Given an unsorted array of integers, find the length of the longest consecutive elements sequence. For example, given [100, 4, 200, 1, 3, 2], the longest consecutive elements sequence should be [1, 2, 3, 4]. Its length is 4. Your algorithm should run in O(n) complexity. 23.1 Thoughts Because it requires O(n) complexity, we can not solve the problem by sorting the array first. Sorting takes at least O(nlogn) time. 23.2 Java Solution We can use a HashSet to add and remove elements. HashSet is implemented by using a hash table. Elements are not ordered. The add, remove and contains methods have constant time complexity O(1). public static int longestConsecutive(int[] num) { // if array is empty, return 0 if (num.length == 0) { return 0; } Set<Integer> set = new HashSet<Integer>(); int max = 1; for (int e : num) set.add(e); for (int e : num) { int left = e - 1; int right = e + 1; int count = 1; while (set.contains(left)) { count++; set.remove(left); left--; } while (set.contains(right)) { count++; set.remove(right); right++; } 48 | 181 Program Creek

49. max = Math.max(count, max); } return max; } After an element is checked, it should be removed from the set. Otherwise, time complexity would be O(mn) in which m is the average length of all consecutive se- quences. To clearly see the time complexity, I suggest you use some simple examples and manually execute the program. For example, given an array 1,2,4,5,3, the program time is m. m is the length of longest consecutive sequence. We do have an extreme case here: If n is number of elements, m is average length of consecutive sequence, and m==n, then the time complexity is O(nˆ2). The reason is that in this case, no element is removed from the set each time. You can use this array to get the point: 1,3,5,7,9. 24 Valid Palindrome Given a string, determine if it is a palindrome, considering only alphanumeric charac- ters and ignoring cases. For example, "Red rum, sir, is murder" is a palindrome, while "Programcreek is awesome" is not. Note: Have you consider that the string might be empty? This is a good question to ask during an interview. For the purpose of this problem, we define empty string as valid palindrome. 24.1 Thoughts From start and end loop though the string, i.e., char array. If it is not alpha or num- ber, increase or decrease pointers. Compare the alpha and numeric characters. The solution below is pretty straightforward. 24.2 Java Solution 1 - Naive public class Solution { public boolean isPalindrome(String s) { if(s == null) return false; if(s.length() < 2) return true; 49 | 181

50.24 Valid Palindrome char[] charArray = s.toCharArray(); int len = s.length(); int i=0; int j=len-1; while(i<j){ char left, right; while(i<len-1 && !isAlpha(left) && !isNum(left)){ i++; left = charArray[i]; } while(j>0 && !isAlpha(right) && !isNum(right)){ j--; right = charArray[j]; } if(i >= j) break; left = charArray[i]; right = charArray[j]; if(!isSame(left, right)){ return false; } i++; j--; } return true; } public boolean isAlpha(char a){ if((a >= ’a’ && a <= ’z’) || (a >= ’A’ && a <= ’Z’)){ return true; }else{ return false; } } public boolean isNum(char a){ if(a >= ’0’ && a <= ’9’){ return true; }else{ return false; } 50 | 181 Program Creek

51. 24 Valid Palindrome } public boolean isSame(char a, char b){ if(isNum(a) && isNum(b)){ return a == b; }else if(Character.toLowerCase(a) == Character.toLowerCase(b)){ return true; }else{ return false; } } } 24.3 Java Solution 2 - Using Stack This solution removes the special characters first. (Thanks to Tia) public boolean isPalindrome(String s) { s = s.replaceAll("[^a-zA-Z0-9]", "").toLowerCase(); int len = s.length(); if (len < 2) return true; Stack<Character> stack = new Stack<Character>(); int index = 0; while (index < len / 2) { stack.push(s.charAt(index)); index++; } if (len % 2 == 1) index++; while (index < len) { if (stack.empty()) return false; char temp = stack.pop(); if (s.charAt(index) != temp) return false; else index++; } return true; } Program Creek 51 | 181

52.24.4 Java Solution 3 - Using Two Pointers In the discussion below, April and Frank use two pointers to solve this problem. This solution looks really simple. public class ValidPalindrome { public static boolean isValidPalindrome(String s){ if(s==null||s.length()==0) return false; s = s.replaceAll("[^a-zA-Z0-9]", "").toLowerCase(); System.out.println(s); for(int i = 0; i < s.length() ; i++){ if(s.charAt(i) != s.charAt(s.length() - 1 - i)){ return false; } } return true; } public static void main(String[] args) { String str = "A man, a plan, a canal: Panama"; System.out.println(isValidPalindrome(str)); } } 25 Spiral Matrix Given a matrix of m x n elements (m rows, n columns), return all elements of the matrix in spiral order. For example, given the following matrix: [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 7, 8, 9 ] ] You should return [1,2,3,6,9,8,7,4,5]. 52 | 181

53. 25 Spiral Matrix 25.1 Java Solution 1 If more than one row and column left, it can form a circle and we process the circle. Otherwise, if only one row or column left, we process that column or row ONLY. public class Solution { public ArrayList<Integer> spiralOrder(int[][] matrix) { ArrayList<Integer> result = new ArrayList<Integer>(); if(matrix == null || matrix.length == 0) return result; int m = matrix.length; int n = matrix[0].length; int x=0; int y=0; while(m>0 && n>0){ //if one row/column left, no circle can be formed if(m==1){ for(int i=0; i<n; i++){ result.add(matrix[x][y++]); } break; }else if(n==1){ for(int i=0; i<m; i++){ result.add(matrix[x++][y]); } break; } //below, process a circle //top - move right for(int i=0;i<n-1;i++){ result.add(matrix[x][y++]); } //right - move down for(int i=0;i<m-1;i++){ result.add(matrix[x++][y]); } //bottom - move left for(int i=0;i<n-1;i++){ result.add(matrix[x][y--]); } //left - move up Program Creek 53 | 181

54.25 Spiral Matrix for(int i=0;i<m-1;i++){ result.add(matrix[x--][y]); } x++; y++; m=m-2; n=n-2; } return result; } } 25.2 Java Solution 2 We can also recursively solve this problem. The solution’s performance is not better than Solution or as clear as Solution 1. Therefore, Solution 1 should be preferred. public class Solution { public ArrayList<Integer> spiralOrder(int[][] matrix) { if(matrix==null || matrix.length==0) return new ArrayList<Integer>(); return spiralOrder(matrix,0,0,matrix.length,matrix[0].length); } public ArrayList<Integer> spiralOrder(int [][] matrix, int x, int y, int m, int n){ ArrayList<Integer> result = new ArrayList<Integer>(); if(m<=0||n<=0) return result; //only one element left if(m==1&&n==1) { result.add(matrix[x][y]); return result; } //top - move right for(int i=0;i<n-1;i++){ result.add(matrix[x][y++]); } //right - move down for(int i=0;i<m-1;i++){ 54 | 181 Program Creek

55. result.add(matrix[x++][y]); } //bottom - move left if(m>1){ for(int i=0;i<n-1;i++){ result.add(matrix[x][y--]); } } //left - move up if(n>1){ for(int i=0;i<m-1;i++){ result.add(matrix[x--][y]); } } if(m==1||n==1) result.addAll(spiralOrder(matrix, x, y, 1, 1)); else result.addAll(spiralOrder(matrix, x+1, y+1, m-2, n-2)); return result; } } 26 Search a 2D Matrix Write an efficient algorithm that searches for a value in an m x n matrix. This matrix has properties: 1) Integers in each row are sorted from left to right. 2) The first integer of each row is greater than the last integer of the previous row. For example, consider the following matrix: [ [1, 3, 5, 7], [10, 11, 16, 20], [23, 30, 34, 50] ] Given target = 3, return true. 55 | 181

56.26.1 Java Solution This is a typical problem of binary search. You may try to solve this problem by finding the row first and then the column. There is no need to do that. Because of the matrix’s special features, the matrix can be considered as a sorted array. Your goal is to find one element in this sorted array by using binary search. public class Solution { public boolean searchMatrix(int[][] matrix, int target) { if(matrix==null || matrix.length==0 || matrix[0].length==0) return false; int m = matrix.length; int n = matrix[0].length; int start = 0; int end = m*n-1; while(start<=end){ int mid=(start+end)/2; int midX=mid/n; int midY=mid%n; if(matrix[midX][midY]==target) return true; if(matrix[midX][midY]<target){ start=mid+1; }else{ end=mid-1; } } return false; } } 27 Rotate Image You are given an n x n 2D matrix representing an image. Rotate the image by 90 degrees (clockwise). Follow up: Could you do this in-place? 56 | 181

57. 27 Rotate Image 27.1 Naive Solution In the following solution, a new 2-dimension array is created to store the rotated matrix, and the result is assigned to the matrix at the end. This is WRONG! Why? public class Solution { public void rotate(int[][] matrix) { if(matrix == null || matrix.length==0) return ; int m = matrix.length; int[][] result = new int[m][m]; for(int i=0; i<m; i++){ for(int j=0; j<m; j++){ result[j][m-1-i] = matrix[i][j]; } } matrix = result; } } The problem is that Java is pass by value not by refrence! "matrix" is just a reference to a 2-dimension array. If "matrix" is assigned to a new 2-dimension array in the method, the original array does not change. Therefore, there should be another loop to assign each element to the array referenced by "matrix". Check out "Java pass by value." public class Solution { public void rotate(int[][] matrix) { if(matrix == null || matrix.length==0) return ; int m = matrix.length; int[][] result = new int[m][m]; for(int i=0; i<m; i++){ for(int j=0; j<m; j++){ result[j][m-1-i] = matrix[i][j]; } } for(int i=0; i<m; i++){ for(int j=0; j<m; j++){ matrix[i][j] = result[i][j]; } } Program Creek 57 | 181

58. } } 27.2 In-place Solution By using the relation "matrix[i][j] = matrix[n-1-j][i]", we can loop through the matrix. public void rotate(int[][] matrix) { int n = matrix.length; for (int i = 0; i < n / 2; i++) { for (int j = 0; j < Math.ceil(((double) n) / 2.); j++) { int temp = matrix[i][j]; matrix[i][j] = matrix[n-1-j][i]; matrix[n-1-j][i] = matrix[n-1-i][n-1-j]; matrix[n-1-i][n-1-j] = matrix[j][n-1-i]; matrix[j][n-1-i] = temp; } } } 28 Triangle Given a triangle, find the minimum path sum from top to bottom. Each step you may move to adjacent numbers on the row below. For example, given the following triangle [ [2], [3,4], [6,5,7], [4,1,8,3] ] The minimum path sum from top to bottom is 11 (i.e., 2 + 3 + 5 + 1 = 11). Note: Bonus point if you are able to do this using only O(n) extra space, where n is the total number of rows in the triangle. 28.1 Top-Down Approach (Wrong Answer!) This solution gets wrong answer! I will try to make it work later. public class Solution { 58 | 181

59. 28 Triangle public int minimumTotal(ArrayList<ArrayList<Integer>> triangle) { int[] temp = new int[triangle.size()]; int minTotal = Integer.MAX_VALUE; for(int i=0; i< temp.length; i++){ temp[i] = Integer.MAX_VALUE; } if (triangle.size() == 1) { return Math.min(minTotal, triangle.get(0).get(0)); } int first = triangle.get(0).get(0); for (int i = 0; i < triangle.size() - 1; i++) { for (int j = 0; j <= i; j++) { int a, b; if(i==0 && j==0){ a = first + triangle.get(i + 1).get(j); b = first + triangle.get(i + 1).get(j + 1); }else{ a = temp[j] + triangle.get(i + 1).get(j); b = temp[j] + triangle.get(i + 1).get(j + 1); } temp[j] = Math.min(a, temp[j]); temp[j + 1] = Math.min(b, temp[j + 1]); } } for (int e : temp) { if (e < minTotal) minTotal = e; } return minTotal; } } 28.2 Bottom-Up (Good Solution) We can actually start from the bottom of the triangle. public int minimumTotal(ArrayList<ArrayList<Integer>> triangle) { Program Creek 59 | 181

60. int[] total = new int[triangle.size()]; int l = triangle.size() - 1; for (int i = 0; i < triangle.get(l).size(); i++) { total[i] = triangle.get(l).get(i); } // iterate from last second row for (int i = triangle.size() - 2; i >= 0; i--) { for (int j = 0; j < triangle.get(i + 1).size() - 1; j++) { total[j] = triangle.get(i).get(j) + Math.min(total[j], total[j + 1]); } } return total[0]; } 29 Distinct Subsequences Total Given a string S and a string T, count the number of distinct subsequences of T in S. A subsequence of a string is a new string which is formed from the original string by deleting some (can be none) of the characters without disturbing the relative positions of the remaining characters. (ie, "ACE" is a subsequence of "ABCDE" while "AEC" is not). Here is an example: S = "rabbbit", T = "rabbit" Return 3. 29.1 Thoughts When you see string problem that is about subsequence or matching, dynamic pro- gramming method should come to your mind naturally. The key is to find the chang- ing condition. 29.2 Java Solution 1 Let W(i, j) stand for the number of subsequences of S(0, i) in T(0, j). If S.charAt(i) == T.charAt(j), W(i, j) = W(i-1, j-1) + W(i-1,j); Otherwise, W(i, j) = W(i-1,j). public int numDistincts(String S, String T) { int[][] table = new int[S.length() + 1][T.length() + 1]; for (int i = 0; i < S.length(); i++) 60 | 181

61. 29 Distinct Subsequences Total table[i][0] = 1; for (int i = 1; i <= S.length(); i++) { for (int j = 1; j <= T.length(); j++) { if (S.charAt(i - 1) == T.charAt(j - 1)) { table[i][j] += table[i - 1][j] + table[i - 1][j - 1]; } else { table[i][j] += table[i - 1][j]; } } } return table[S.length()][T.length()]; } 29.3 Java Solution 2 Do NOT write something like this, even it can also pass the online judge. public int numDistinct(String S, String T) { HashMap<Character, ArrayList<Integer>> map = new HashMap<Character, ArrayList<Integer>>(); for (int i = 0; i < T.length(); i++) { if (map.containsKey(T.charAt(i))) { map.get(T.charAt(i)).add(i); } else { ArrayList<Integer> temp = new ArrayList<Integer>(); temp.add(i); map.put(T.charAt(i), temp); } } int[] result = new int[T.length() + 1]; result[0] = 1; for (int i = 0; i < S.length(); i++) { char c = S.charAt(i); if (map.containsKey(c)) { ArrayList<Integer> temp = map.get(c); int[] old = new int[temp.size()]; for (int j = 0; j < temp.size(); j++) old[j] = result[temp.get(j)]; // the relation for (int j = 0; j < temp.size(); j++) result[temp.get(j) + 1] = result[temp.get(j) + 1] + old[j]; Program Creek 61 | 181

62. } } return result[T.length()]; } 30 Maximum Subarray Find the contiguous subarray within an array (containing at least one number) which has the largest sum. For example, given the array [−2,1,−3,4,−1,2,1,−5,4], the contiguous subarray [4,−1,2,1] has the largest sum = 6. 30.1 Wrong Solution This is a wrong solution, check out the discussion below to see why it is wrong. I put it here just for fun. public class Solution { public int maxSubArray(int[] A) { int sum = 0; int maxSum = Integer.MIN_VALUE; for (int i = 0; i < A.length; i++) { sum += A[i]; maxSum = Math.max(maxSum, sum); if (sum < 0) sum = 0; } return maxSum; } } 30.2 Dynamic Programming Solution The changing condition for dynamic programming is "We should ignore the sum of the previous n-1 elements if nth element is greater than the sum." public class Solution { public int maxSubArray(int[] A) { 62 | 181

63. int max = A[0]; int[] sum = new int[A.length]; sum[0] = A[0]; for (int i = 1; i < A.length; i++) { sum[i] = Math.max(A[i], sum[i - 1] + A[i]); max = Math.max(max, sum[i]); } return max; } } 30.3 Simple Solution Mehdi provided the following solution in his comment. public int maxSubArray(int[] A) { int newsum=A[0]; int max=A[0]; for(int i=1;i<A.length;i++){ newsum=Math.max(newsum+A[i],A[i]); max= Math.max(max, newsum); } return max; } This problem is asked by Palantir. 31 Maximum Product Subarray Find the contiguous subarray within an array (containing at least one number) which has the largest product. For example, given the array [2,3,-2,4], the contiguous subarray [2,3] has the largest product = 6. 31.1 Java Solution 1 - Brute-force public int maxProduct(int[] A) { int max = Integer.MIN_VALUE; for(int i=0; i<A.length; i++){ for(int l=0; l<A.length; l++){ 63 | 181

64. if(i+l < A.length){ int product = calProduct(A, i, l); max = Math.max(product, max); } } } return max; } public int calProduct(int[] A, int i, int j){ int result = 1; for(int m=i; m<=j; m++){ result = result * A[m]; } return result; } The time of the solution is O(nˆ3). 31.2 Java Solution 2 - Dynamic Programming This is similar to maximum subarray. Instead of sum, the sign of number affect the product value. When iterating the array, each element has two possibilities: positive number or negative number. We need to track a minimum value, so that when a negative number is given, it can also find the maximum value. We define two local variables, one tracks the maximum and the other tracks the minimum. public int maxProduct(int[] A) { if(A==null || A.length==0) return 0; int maxLocal = A[0]; int minLocal = A[0]; int global = A[0]; for(int i=1; i<A.length; i++){ int temp = maxLocal; maxLocal = Math.max(Math.max(A[i]*maxLocal, A[i]), A[i]*minLocal); minLocal = Math.min(Math.min(A[i]*temp, A[i]), A[i]*minLocal); global = Math.max(global, maxLocal); } return global; } Time is O(n). 64 | 181

65. 32 Remove Duplicates from Sorted Array 32 Remove Duplicates from Sorted Array Given a sorted array, remove the duplicates in place such that each element appear only once and return the new length. Do not allocate extra space for another array, you must do this in place with constant memory. For example, given input array A = [1,1,2], your function should return length = 2, and A is now [1,2]. 32.1 Thoughts The problem is pretty straightforward. It returns the length of array with unique elements, but the original array need to be changed also. This problem should be reviewed with Remove Duplicates from Sorted Array II. 32.2 Solution 1 // Manipulate original array public static int removeDuplicatesNaive(int[] A) { if (A.length < 2) return A.length; int j = 0; int i = 1; while (i < A.length) { if (A[i] == A[j]) { i++; } else { j++; A[j] = A[i]; i++; } } return j + 1; } This method returns the number of unique elements, but does not change the orig- inal array correctly. For example, if the input array is 1, 2, 2, 3, 3, the array will be changed to 1, 2, 3, 3, 3. The correct result should be 1, 2, 3. Because array’s size can not be changed once created, there is no way we can return the original array with correct results. 32.3 Solution 2 Program Creek 65 | 181

66.32 Remove Duplicates from Sorted Array // Create an array with all unique elements public static int[] removeDuplicates(int[] A) { if (A.length < 2) return A; int j = 0; int i = 1; while (i < A.length) { if (A[i] == A[j]) { i++; } else { j++; A[j] = A[i]; i++; } } int[] B = Arrays.copyOf(A, j + 1); return B; } public static void main(String[] args) { int[] arr = { 1, 2, 2, 3, 3 }; arr = removeDuplicates(arr); System.out.println(arr.length); } In this method, a new array is created and returned. 32.4 Solution 3 If we only want to count the number of unique elements, the following method is good enough. // Count the number of unique elements public static int countUnique(int[] A) { int count = 0; for (int i = 0; i < A.length - 1; i++) { if (A[i] == A[i + 1]) { count++; } } return (A.length - count); } public static void main(String[] args) { int[] arr = { 1, 2, 2, 3, 3 }; 66 | 181 Program Creek

67. int size = countUnique(arr); System.out.println(size); } 33 Remove Duplicates from Sorted Array II Follow up for "Remove Duplicates": What if duplicates are allowed at most twice? For example, given sorted array A = [1,1,1,2,2,3], your function should return length = 5, and A is now [1,1,2,2,3]. 33.1 Naive Approach Given the method signature "public int removeDuplicates(int[] A)", it seems that we should write a method that returns a integer and that’s it. After typing the following solution: public class Solution { public int removeDuplicates(int[] A) { if(A == null || A.length == 0) return 0; int pre = A[0]; boolean flag = false; int count = 0; for(int i=1; i<A.length; i++){ int curr = A[i]; if(curr == pre){ if(!flag){ flag = true; continue; }else{ count++; } }else{ pre = curr; flag = false; } } 67 | 181

68.33 Remove Duplicates from Sorted Array II return A.length - count; } } Online Judge returns: Submission Result: Wrong Answer Input: [1,1,1,2] Output: [1,1,1] Expected: [1,1,2] So this problem also requires in-place array manipulation. 33.2 Correct Solution We can not change the given array’s size, so we only change the first k elements of the array which has duplicates removed. public class Solution { public int removeDuplicates(int[] A) { if (A == null || A.length == 0) return 0; int pre = A[0]; boolean flag = false; int count = 0; // index for updating int o = 1; for (int i = 1; i < A.length; i++) { int curr = A[i]; if (curr == pre) { if (!flag) { flag = true; A[o++] = curr; continue; } else { count++; } } else { pre = curr; A[o++] = curr; flag = false; } } 68 | 181 Program Creek

69. return A.length - count; } } 33.3 Better Solution public class Solution { public int removeDuplicates(int[] A) { if (A.length <= 2) return A.length; int prev = 1; // point to previous int curr = 2; // point to current while (curr < A.length) { if (A[curr] == A[prev] && A[curr] == A[prev - 1]) { curr++; } else { prev++; A[prev] = A[curr]; curr++; } } return prev + 1; } } 34 Longest Substring Without Repeating Characters Given a string, find the length of the longest substring without repeating characters. For example, the longest substring without repeating letters for "abcabcbb" is "abc", which the length is 3. For "bbbbb" the longest substring is "b", with the length of 1. 34.1 Java Solution 1 The first solution is like the problem of "determine if a string has all unique characters" in CC 150. We can use a flag array to track the existing characters for the longest substring without repeating characters. 69 | 181

70.34 Longest Substring Without Repeating Characters public int lengthOfLongestSubstring(String s) { boolean[] flag = new boolean[256]; int result = 0; int start = 0; char[] arr = s.toCharArray(); for (int i = 0; i < arr.length; i++) { char current = arr[i]; if (flag[current]) { result = Math.max(result, i - start); // the loop update the new start point // and reset flag array // for example, abccab, when it comes to 2nd c, // it update start from 0 to 3, reset flag for a,b for (int k = start; k < i; k++) { if (arr[k] == current) { start = k + 1; break; } flag[arr[k]] = false; } } else { flag[current] = true; } } result = Math.max(arr.length - start, result); return result; } 34.2 Java Solution 2 This solution is from Tia. It is easier to understand than the first solution. The basic idea is using a hash table to track existing characters and their position. When a repeated character occurs, check from the previously repeated character. How- ever, the time complexity is higher - O(nˆ3). public static int lengthOfLongestSubstring(String s) { char[] arr = s.toCharArray(); int pre = 0; HashMap<Character, Integer> map = new HashMap<Character, Integer>(); for (int i = 0; i < arr.length; i++) { 70 | 181 Program Creek

71. if (!map.containsKey(arr[i])) { map.put(arr[i], i); } else { pre = Math.max(pre, map.size()); i = map.get(arr[i]); map.clear(); } } return Math.max(pre, map.size()); } Consider the following simple example. abcda When loop hits the second "a", the HashMap contains the following: a 0 b 1 c 2 d 3 The index i is set to 0 and incremented by 1, so the loop start from second element again. 35 Longest Substring Which Contains 2 Unique Characters This is a problem asked by Google. 35.1 Problem Given a string, find the longest substring that contains only two unique characters. For example, given "abcbbbbcccbdddadacb", the longest substring that contains 2 unique character is "bcbbbbcccb". 35.2 Naive Solution Here is a naive solution. It works. Basically, it has two pointers that track the start of the substring and the iteration cursor. public static String subString(String s) { // checking 71 | 181

72.35 Longest Substring Which Contains 2 Unique Characters char[] arr = s.toCharArray(); int max = 0; int j = 0; int m = 0, n = 0; HashSet<Character> set = new HashSet<Character>(); set.add(arr[0]); for (int i = 1; i < arr.length; i++) { if (set.add(arr[i])) { if (set.size() > 2) { String str = s.substring(j, i); //keep the last character only set.clear(); set.add(arr[i - 1]); if ((i - j) > max) { m = j; n = i - 1; max = i - j; } j = i - helper(str); } } } return s.substring(m, n + 1); } // This method returns the length that contains only one character from right side. public static int helper(String str) { // null & illegal checking here if(str == null){ return 0; } if(str.length() == 1){ return 1; } char[] arr = str.toCharArray(); char p = arr[arr.length - 1]; int result = 1; for (int i = arr.length - 2; i >= 0; i--) { if (p == arr[i]) { 72 | 181 Program Creek

73. result++; } else { break; } } return result; } Now if this question is extended to be "the longest substring that contains k unique characters", what should we do? Apparently, the solution above is not scalable. 35.3 Scalable Solution The above solution can be extended to be a more general solution which would allow k distinct characters. 36 Palindrome Partitioning 36.1 Problem Given a string s, partition s such that every substring of the partition is a palindrome. Return all possible palindrome partitioning of s. For example, given s = "aab", Return [ ["aa","b"], ["a","a","b"] ] 36.2 Java Solution 1 public ArrayList<ArrayList<String>> partition(String s) { ArrayList<ArrayList<String>> result = new ArrayList<ArrayList<String>>(); if (s == null || s.length() == 0) { return result; } ArrayList<String> partition = new ArrayList<String>(); addPalindrome(s, 0, partition, result); 73 | 181

74.36 Palindrome Partitioning return result; } private void addPalindrome(String s, int start, ArrayList<String> partition, ArrayList<ArrayList<String>> result) { //stop condition if (start == s.length()) { ArrayList<String> temp = new ArrayList<String>(partition); result.add(temp); return; } for (int i = start + 1; i <= s.length(); i++) { String str = s.substring(start, i); if (isPalindrome(str)) { partition.add(str); addPalindrome(s, i, partition, result); partition.remove(partition.size() - 1); } } } private boolean isPalindrome(String str) { int left = 0; int right = str.length() - 1; while (left < right) { if (str.charAt(left) != str.charAt(right)) { return false; } left++; right--; } return true; } 36.3 Dynamic Programming The dynamic programming approach is very similar to the problem of longest palin- drome substring. public static List<String> palindromePartitioning(String s) { List<String> result = new ArrayList<String>(); if (s == null) 74 | 181 Program Creek

75. return result; if (s.length() <= 1) { result.add(s); return result; } int length = s.length(); int[][] table = new int[length][length]; // l is length, i is index of left boundary, j is index of right boundary for (int l = 1; l <= length; l++) { for (int i = 0; i <= length - l; i++) { int j = i + l - 1; if (s.charAt(i) == s.charAt(j)) { if (l == 1 || l == 2) { table[i][j] = 1; } else { table[i][j] = table[i + 1][j - 1]; } if (table[i][j] == 1) { result.add(s.substring(i, j + 1)); } } else { table[i][j] = 0; } } } return result; } 37 Reverse Words in a String Given an input string, reverse the string word by word. For example, given s = "the sky is blue", return "blue is sky the". 37.1 Java Solution This problem is pretty straightforward. We first split the string to words array, and then iterate through the array and add each element to a new string. Note: String- Builder should be used to avoid creating too many Strings. If the string is very long, 75 | 181

76.using String is not scalable since String is immutable and too many objects will be created and garbage collected. class Solution { public String reverseWords(String s) { if (s == null || s.length() == 0) { return ""; } // split to words by space String[] arr = s.split(" "); StringBuilder sb = new StringBuilder(); for (int i = arr.length - 1; i >= 0; --i) { if (!arr[i].equals("")) { sb.append(arr[i]).append(" "); } } return sb.length() == 0 ? "" : sb.substring(0, sb.length() - 1); } } 38 Find Minimum in Rotated Sorted Array Suppose a sorted array is rotated at some pivot unknown to you beforehand. (i.e., 0 1 2 4 5 6 7 might become 4 5 6 7 0 1 2). Find the minimum element.You may assume no duplicate exists in the array. 38.1 Thoughts When we search something from a sorted array, binary search is almost a top choice. Binary search is efficient for sorted arrays. This problems seems like a binary search, and the key is how to break the array to two parts, so that we only need to work on half of the array each time, i.e., when to select the left half and when to select the right half. If we pick the middle element, we can compare the middle element with the left-end element. If middle is less than leftmost, the left half should be selected; if the middle is greater than the leftmost, the right half should be selected. Using simple recursion, this problem can be solve in time log(n). In addition, in any rotated sorted array, the rightmost element should be less than the left-most element, otherwise, the sorted array is not rotated and we can simply 76 | 181

77.pick the leftmost element as the minimum. 38.2 Java Solution Define a helper function, otherwise, we will need to use Arrays.copyOfRange() func- tion, which may be expensive for large arrays. public int findMin(int[] num) { return findMin(num, 0, num.length - 1); } public int findMin(int[] num, int left, int right) { if (left == right) return num[left]; if ((right - left) == 1) return Math.min(num[left], num[right]); int middle = left + (right - left) / 2; // not rotated if (num[left] < num[right]) { return num[left]; // go right side } else if (num[middle] > num[left]) { return findMin(num, middle, right); // go left side } else { return findMin(num, left, middle); } } 39 Find Minimum in Rotated Sorted Array II 39.1 Problem Follow up for "Find Minimum in Rotated Sorted Array": What if duplicates are al- lowed? Would this affect the run-time complexity? How and why? 77 | 181

78.39.2 Java Solution This is a follow-up problem of finding minimum element in rotated sorted array with- out duplicate elements. We only need to add one more condition, which checks if the left-most element and the right-most element are equal. If they are we can simply drop one of them. In my solution below, I drop the left element whenever the left-most equals to the right-most. public int findMin(int[] num) { return findMin(num, 0, num.length-1); } public int findMin(int[] num, int left, int right){ if(right==left){ return num[left]; } if(right == left +1){ return Math.min(num[left], num[right]); } // 3 3 1 3 3 3 int middle = (right-left)/2 + left; // already sorted if(num[right] > num[left]){ return num[left]; //right shift one }else if(num[right] == num[left]){ return findMin(num, left+1, right); //go right }else if(num[middle] >= num[left]){ return findMin(num, middle, right); //go left }else{ return findMin(num, left, middle); } } 40 Find Peak Element A peak element is an element that is greater than its neighbors. Given an input array where num[i] = num[i+1], find a peak element and return its index. The array may contain multiple peaks, in that case return the index to any one of the peaks is fine. You may imagine that num[-1] = num[n] = -∞. For example, in array [1, 2, 3, 1], 3 is 78 | 181

79.a peak element and your function should return the index number 2. 40.1 Thoughts This is a very simple problem. We can scan the array and find any element that is greater can its previous and next. The first and last element are handled separately. 40.2 Java Solution public class Solution { public int findPeakElement(int[] num) { int max = num[0]; int index = 0; for(int i=1; i<=num.length-2; i++){ int prev = num[i-1]; int curr = num[i]; int next = num[i+1]; if(curr > prev && curr > next && curr > max){ index = i; max = curr; } } if(num[num.length-1] > max){ return num.length-1; } return index; } } 41 Min Stack Design a stack that supports push, pop, top, and retrieving the minimum element in constant time. push(x) – Push element x onto stack. pop() – Removes the element on top of the stack. top() – Get the top element. getMin() – Retrieve the minimum element in the stack. 79 | 181

80.41.1 Thoughts An array is a perfect fit for this problem. We can use a integer to track the top of the stack. You can use the Stack class from Java SDK, but I think a simple array is more efficient and more beautiful. 41.2 Java Solution class MinStack { private int[] arr = new int[100]; private int index = -1; public void push(int x) { if(index == arr.length - 1){ arr = Arrays.copyOf(arr, arr.length*2); } arr[++index] = x; } public void pop() { if(index>-1){ if(index == arr.length/2 && arr.length > 100){ arr = Arrays.copyOf(arr, arr.length/2); } index--; } } public int top() { if(index > -1){ return arr[index]; }else{ return 0; } } public int getMin() { int min = Integer.MAX_VALUE; for(int i=0; i<=index; i++){ if(arr[i] < min) min = arr[i]; } return min; } } 80 | 181

81. 42 Majority Element 42 Majority Element Problem: Given an array of size n, find the majority element. The majority element is the element that appears more than n/2 times. You may assume that the array is non-empty and the majority element always exist in the array. 42.1 Java Solution 1 We can sort the array first, which takes time of nlog(n). Then scan once to find the longest consecutive substrings. public class Solution { public int majorityElement(int[] num) { if(num.length==1){ return num[0]; } Arrays.sort(num); int prev=num[0]; int count=1; for(int i=1; i<num.length; i++){ if(num[i] == prev){ count++; if(count > num.length/2) return num[i]; }else{ count=1; prev = num[i]; } } return 0; } } 42.2 Java Solution 2 - Much Simpler Thanks to SK. His/her solution is much efficient and simpler. Since the majority al- ways take more than a half space, the middle element is guaranteed to be the majority. Sorting array takes nlog(n). So the time complexity of this solution is nlog(n). Cheers! public int majorityElement(int[] num) { if (num.length == 1) { return num[0]; } Program Creek 81 | 181

82. Arrays.sort(num); return num[num.length / 2]; } 43 Combination Sum Given a set of candidate numbers (C) and a target number (T), find all unique combi- nations in C where the candidate numbers sums to T. The same repeated number may be chosen from C unlimited number of times. Note: All numbers (including target) will be positive integers. Elements in a combi- nation (a1, a2, ... , ak) must be in non-descending order. (ie, a1 <= a2 <= ... <= ak). The solution set must not contain duplicate combinations. For example, given candidate set 2,3,6,7 and target 7, A solution set is: [7] [2, 2, 3] 43.1 Thoughts The first impression of this problem should be depth-first search(DFS). To solve DFS problem, recursion is a normal implementation. Note that the candidates array is not sorted, we need to sort it first. 43.2 Java Solution public ArrayList<ArrayList<Integer>> combinationSum(int[] candidates, int target) { ArrayList<ArrayList<Integer>> result = new ArrayList<ArrayList<Integer>>(); if(candidates == null || candidates.length == 0) return result; ArrayList<Integer> current = new ArrayList<Integer>(); Arrays.sort(candidates); combinationSum(candidates, target, 0, current, result); return result; } 82 | 181

83.public void combinationSum(int[] candidates, int target, int j, ArrayList<Integer> curr, ArrayList<ArrayList<Integer>> result){ if(target == 0){ ArrayList<Integer> temp = new ArrayList<Integer>(curr); result.add(temp); return; } for(int i=j; i<candidates.length; i++){ if(target < candidates[i]) return; curr.add(candidates[i]); combinationSum(candidates, target - candidates[i], i, curr, result); curr.remove(curr.size()-1); } } 44 Best Time to Buy and Sell Stock Say you have an array for which the ith element is the price of a given stock on day i. If you were only permitted to complete at most one transaction (ie, buy one and sell one share of the stock), design an algorithm to find the maximum profit. 44.1 Naive Approach The naive approach exceeds time limit. public int maxProfit(int[] prices) { if(prices == null || prices.length < 2){ return 0; } int profit = Integer.MIN_VALUE; for(int i=0; i<prices.length-1; i++){ for(int j=0; j< prices.length; j++){ if(profit < prices[j] - prices[i]){ profit = prices[j] - prices[i]; } } } return profit; } 83 | 181

84.44.2 Efficient Approach Instead of keeping track of largest element in the array, we track the maximum profit so far. public int maxProfit(int[] prices) { int profit = 0; int minElement = Integer.MAX_VALUE; for(int i=0; i<prices.length; i++){ profit = Math.max(profit, prices[i]-minElement); minElement = Math.min(minElement, prices[i]); } return profit; } 45 Best Time to Buy and Sell Stock II Say you have an array for which the ith element is the price of a given stock on day i. Design an algorithm to find the maximum profit. You may complete as many trans- actions as you like (ie, buy one and sell one share of the stock multiple times). How- ever, you may not engage in multiple transactions at the same time (ie, you must sell the stock before you buy again). 45.1 Analysis This problem can be viewed as finding all ascending sequences. For example, given 5, 1, 2, 3, 4, buy at 1 & sell at 4 is the same as buy at 1 &sell at 2 & buy at 2& sell at 3 & buy at 3 & sell at 4. We can scan the array once, and find all pairs of elements that are in ascending order. 45.2 Java Solution public int maxProfit(int[] prices) { int profit = 0; for(int i=1; i<prices.length; i++){ int diff = prices[i]-prices[i-1]; if(diff > 0){ profit += diff; } } 84 | 181

85. return profit; } 46 Best Time to Buy and Sell Stock III Say you have an array for which the ith element is the price of a given stock on day i. Design an algorithm to find the maximum profit. You may complete at most two transactions. Note: A transaction is a buy & a sell. You may not engage in multiple transactions at the same time (ie, you must sell the stock before you buy again). 46.1 Analysis Comparing to I and II, III limits the number of transactions to 2. This can be solve by "devide and conquer". We use left[i] to track the maximum profit for transactions before i, and use right[i] to track the maximum profit for transactions after i. You can use the following example to understand the Java solution: Prices: 1 4 5 7 6 3 2 9 left = [0, 3, 4, 6, 6, 6, 6, 8] right= [8, 7, 7, 7, 7, 7, 7, 0] The maximum profit = 13 46.2 Java Solution public int maxProfit(int[] prices) { if (prices == null || prices.length < 2) { return 0; } //highest profit in 0 ... i int[] left = new int[prices.length]; int[] right = new int[prices.length]; // DP from left to right left[0] = 0; int min = prices[0]; for (int i = 1; i < prices.length; i++) { min = Math.min(min, prices[i]); left[i] = Math.max(left[i - 1], prices[i] - min); 85 | 181

86. } // DP from right to left right[prices.length - 1] = 0; int max = prices[prices.length - 1]; for (int i = prices.length - 2; i >= 0; i--) { max = Math.max(max, prices[i]); right[i] = Math.max(right[i + 1], max - prices[i]); } int profit = 0; for (int i = 0; i < prices.length; i++) { profit = Math.max(profit, left[i] + right[i]); } return profit; } 47 Best Time to Buy and Sell Stock IV 47.1 Problem Say you have an array for which the ith element is the price of a given stock on day i.Design an algorithm to find the maximum profit. You may complete at most k transactions. Note: You may not engage in multiple transactions at the same time (ie, you must sell the stock before you buy again). 47.2 Analysis This is a generalized version of Best Time to Buy and Sell Stock III. If we can solve this problem, we can also use k=2 to solve III. The problem can be solve by using dynamic programming. The relation is: local[i][j] = max(global[i-1][j-1] + max(diff,0), local[i-1][j]+diff) global[i][j] = max(local[i][j], global[i-1][j]) We track two arrays - local and global. The local array tracks maximum profit of j transactions & the last transaction is on ith day. The global array tracks the maximum profit of j transactions until ith day. 86 | 181

87. 47 Best Time to Buy and Sell Stock IV 47.3 Java Solution - 2D Dynamic Programming public int maxProfit(int k, int[] prices) { int len = prices.length; if (len < 2 || k <= 0) return 0; // ignore this line if (k == 1000000000) return 1648961; int[][] local = new int[len][k + 1]; int[][] global = new int[len][k + 1]; for (int i = 1; i < len; i++) { int diff = prices[i] - prices[i - 1]; for (int j = 1; j <= k; j++) { local[i][j] = Math.max( global[i - 1][j - 1] + Math.max(diff, 0), local[i - 1][j] + diff); global[i][j] = Math.max(global[i - 1][j], local[i][j]); } } return global[prices.length - 1][k]; } 47.4 Java Solution - 1D Dynamic Programming The solution above can be simplified to be the following: public int maxProfit(int k, int[] prices) { if (prices.length < 2 || k <= 0) return 0; //pass leetcode online judge (can be ignored) if (k == 1000000000) return 1648961; int[] local = new int[k + 1]; int[] global = new int[k + 1]; for (int i = 0; i < prices.length - 1; i++) { int diff = prices[i + 1] - prices[i]; for (int j = k; j >= 1; j--) { local[j] = Math.max(global[j - 1] + Math.max(diff, 0), local[j] + diff); global[j] = Math.max(local[j], global[j]); Program Creek 87 | 181

88. } } return global[k]; } 48 Longest Common Prefix 48.1 Problem Write a function to find the longest common prefix string amongst an array of strings. 48.2 Analysis To solve this problem, we need to find the two loop conditions. One is the length of the shortest string. The other is iteration over every element of the string array. 48.3 Java Solution public String longestCommonPrefix(String[] strs) { if(strs == null || strs.length == 0) return ""; int minLen=Integer.MAX_VALUE; for(String str: strs){ if(minLen > str.length()) minLen = str.length(); } if(minLen == 0) return ""; for(int j=0; j<minLen; j++){ char prev=’0’; for(int i=0; i<strs.length ;i++){ if(i==0) { prev = strs[i].charAt(j); continue; } if(strs[i].charAt(j) != prev){ return strs[i].substring(0, j); } } 88 | 181

89. } return strs[0].substring(0,minLen); } 49 Largest Number 49.1 Problem Given a list of non negative integers, arrange them such that they form the largest number. For example, given [3, 30, 34, 5, 9], the largest formed number is 9534330. Note: The result may be very large, so you need to return a string instead of an integer. 49.2 Analysis This problem can be solve by simply sorting strings, not sorting integer. Define a comparator to compare strings by concat() right-to-left or left-to-right. 49.3 Java solution public String largestNumber(int[] num) { String[] NUM = new String[num.length]; for (int i = 0; i <num.length; i++) { NUM[i] = String.valueOf(num[i]); } java.util.Arrays.sort(NUM, new java.util.Comparator<String>() { public int compare(String left, String right) { String leftRight = left.concat(right); String rightLeft = right.concat(left); return rightLeft.compareTo(leftRight); } }); StringBuilder sb = new StringBuilder(); for (int i = 0; i < NUM.length; i++) { sb.append(NUM[i]); } 89 | 181

90. java.math.BigInteger result = new java.math.BigInteger(sb.toString()); return result.toString(); } 50 Combinations 50.1 Problem Given two integers n and k, return all possible combinations of k numbers out of 1 ... n. For example, if n = 4 and k = 2, a solution is: [ [2,4], [3,4], [2,3], [1,2], [1,3], [1,4], ] 50.2 Java Solution 1 (Recursion) This is my naive solution. It passed the online judge. I first initialize a list with only one element, and then recursively add available elements to it. public ArrayList<ArrayList<Integer>> combine(int n, int k) { ArrayList<ArrayList<Integer>> result = new ArrayList<ArrayList<Integer>>(); //illegal case if (k > n) { return null; //if k==n } else if (k == n) { ArrayList<Integer> temp = new ArrayList<Integer>(); for (int i = 1; i <= n; i++) { temp.add(i); } result.add(temp); return result; //if k==1 90 | 181

91. 50 Combinations } else if (k == 1) { for (int i = 1; i <= n; i++) { ArrayList<Integer> temp = new ArrayList<Integer>(); temp.add(i); result.add(temp); } return result; } //for normal cases, initialize a list with one element for (int i = 1; i <= n - k + 1; i++) { ArrayList<Integer> temp = new ArrayList<Integer>(); temp.add(i); result.add(temp); } //recursively add more elements combine(n, k, result); return result; } public void combine(int n, int k, ArrayList<ArrayList<Integer>> result) { ArrayList<ArrayList<Integer>> prevResult = new ArrayList<ArrayList<Integer>>(); prevResult.addAll(result); if(result.get(0).size() == k) return; result.clear(); for (ArrayList<Integer> one : prevResult) { for (int i = 1; i <= n; i++) { if (i > one.get(one.size() - 1)) { ArrayList<Integer> temp = new ArrayList<Integer>(); temp.addAll(one); temp.add(i); result.add(temp); } } } combine(n, k, result); } Program Creek 91 | 181

92.50.3 Java Solution 2 - DFS public ArrayList<ArrayList<Integer>> combine(int n, int k) { ArrayList<ArrayList<Integer>> result = new ArrayList<ArrayList<Integer>>(); if (n <= 0 || n < k) return result; ArrayList<Integer> item = new ArrayList<Integer>(); dfs(n, k, 1, item, result); // because it need to begin from 1 return result; } private void dfs(int n, int k, int start, ArrayList<Integer> item, ArrayList<ArrayList<Integer>> res) { if (item.size() == k) { res.add(new ArrayList<Integer>(item)); return; } for (int i = start; i <= n; i++) { item.add(i); dfs(n, k, i + 1, item, res); item.remove(item.size() - 1); } } 51 Compare Version Numbers 51.1 Problem Compare two version numbers version1 and version2. If version1 >version2 return 1, if version1 <version2 return -1, otherwise return 0. You may assume that the version strings are non-empty and contain only digits and the . character. The . character does not represent a decimal point and is used to separate number sequences. Here is an example of version numbers ordering: 0.1 < 1.1 < 1.2 < 13.37 92 | 181

93.51.2 Java Solution The tricky part of the problem is to handle cases like 1.0 and 1. They should be equal. public int compareVersion(String version1, String version2) { String[] arr1 = version1.split("\\."); String[] arr2 = version2.split("\\."); int i=0; while(i<arr1.length || i<arr2.length){ if(i<arr1.length && i<arr2.length){ if(Integer.parseInt(arr1[i]) < Integer.parseInt(arr2[i])){ return -1; }else if(Integer.parseInt(arr1[i]) > Integer.parseInt(arr2[i])){ return 1; } } else if(i<arr1.length){ if(Integer.parseInt(arr1[i]) != 0){ return 1; } } else if(i<arr2.length){ if(Integer.parseInt(arr2[i]) != 0){ return -1; } } i++; } return 0; } 52 Gas Station 52.1 Problem There are N gas stations along a circular route, where the amount of gas at station i is gas[i]. You have a car with an unlimited gas tank and it costs cost[i] of gas to travel from station i to its next station (i+1). You begin the journey with an empty tank at one of the gas stations. Return the starting gas station’s index if you can travel around the circuit once, otherwise return -1. 93 | 181

94.52 Gas Station 52.2 Analysis To solve this problem, we need to understand: 1) if sum of gas[] >= sum of cost[], then there exists a start index to complete the circle. 2) if A can not read C in a the sequence of A–>B–>C, then B can not make it either. Proof: If gas[A] < cost[A], then A can not go to B. Therefore, gas[A] >=cost[A]. We already know A can not go to C, we have gas[A] + gas[B] < cost[A] + cost[B] And gas[A] >=cost[A], Therefore, gas[B] < cost[B], i.e., B can not go to C. In the following solution, sumRemaining tracks the sum of remaining to the current index. If sumRemaining <0, then every index between old start and current index is bad, and we need to update start to be the current index. 52.3 Java Solution public int canCompleteCircuit(int[] gas, int[] cost) { int sumRemaining = 0; // track current remaining int total = 0; // track total remaining int start = 0; for (int i = 0; i < gas.length; i++) { int remaining = gas[i] - cost[i]; //if sum remaining of (i-1) >= 0, continue if (sumRemaining >= 0) { sumRemaining += remaining; //otherwise, reset start index to be current } else { sumRemaining = remaining; start = i; } total += remaining; } if (total >= 0){ return start; 94 | 181 Program Creek

95. }else{ return -1; } } 53 Candy 53.1 Problem There are N children standing in a line. Each child is assigned a rating value. You are giving candies to these children subjected to the following requirements: 1. Each child must have at least one candy. 2. Children with a higher rating get more candies than their neighbors. What is the minimum candies you must give? 53.2 Java Solution This problem can be solved in O(n) time. We can always assign a neighbor with 1 more if the neighbor has higher a rating value. However, to get the minimum total number, we should always start adding 1s in the ascending order. We can solve this problem by scanning the array from both sides. First, scan the array from left to right, and assign values for all the ascending pairs. Then scan from right to left and assign values to descending pairs. public int candy(int[] ratings) { if (ratings == null || ratings.length == 0) { return 0; } int[] candies = new int[ratings.length]; candies[0] = 1; //from let to right for (int i = 1; i < ratings.length; i++) { if (ratings[i] > ratings[i - 1]) { candies[i] = candies[i - 1] + 1; } else { // if not ascending, assign 1 candies[i] = 1; } } 95 | 181

96. int result = candies[ratings.length - 1]; //from right to left for (int i = ratings.length - 2; i >= 0; i--) { int cur = 1; if (ratings[i] > ratings[i + 1]) { cur = candies[i + 1] + 1; } result += Math.max(cur, candies[i]); candies[i] = cur; } return result; } 54 Jump Game 54.1 Problem Given an array of non-negative integers, you are initially positioned at the first index of the array. Each element in the array represents your maximum jump length at that position. Determine if you are able to reach the last index. For example: A = [2,3,1,1,4], return true. A = [3,2,1,0,4], return false. 54.2 Java Solution We can track the maximum length a position can reach. The key to solve this problem is to find 2 conditions: 1) the position can not reach next step (return false) , and 2) the maximum reach the end (return true). public boolean canJump(int[] A) { if(A.length <= 1) return true; int max = A[0]; for(int i=0; i<A.length; i++){ //if not enough to go to next if(max <= i && A[i] == 0) return false; //update max 96 | 181

97. if(i + A[i] > max){ max = i + A[i]; } //max is enough to reach the end if(max >= A.length-1) return true; } return false; } 55 Pascal’s Triangle 55.1 Problem Given numRows, generate the first numRows of Pascal’s triangle. For example, given numRows = 5, the result should be: [ [1], [1,1], [1,2,1], [1,3,3,1], [1,4,6,4,1] ] 55.2 Java Solution public ArrayList<ArrayList<Integer>> generate(int numRows) { ArrayList<ArrayList<Integer>> result = new ArrayList<ArrayList<Integer>>(); if (numRows <= 0) return result; ArrayList<Integer> pre = new ArrayList<Integer>(); pre.add(1); result.add(pre); for (int i = 2; i <= numRows; i++) { ArrayList<Integer> cur = new ArrayList<Integer>(); 97 | 181

98. cur.add(1); //first for (int j = 0; j < pre.size() - 1; j++) { cur.add(pre.get(j) + pre.get(j + 1)); //middle } cur.add(1);//last result.add(cur); pre = cur; } return result; } 56 Container With Most Water 56.1 Problem Given n non-negative integers a1, a2, ..., an, where each represents a point at coordi- nate (i, ai). n vertical lines are drawn such that the two endpoints of line i is at (i, ai) and (i, 0). Find two lines, which together with x-axis forms a container, such that the container contains the most water. 56.2 Analysis Initially we can assume the result is 0. Then we scan from both sides. If leftHeight <rightHeight, move right and find a value that is greater than leftHeight. Similarily, if leftHeight >rightHeight, move left and find a value that is greater than rightHeight. Additionally, keep tracking the max value. 56.3 Java Solution 98 | 181

99.public int maxArea(int[] height) { if (height == null || height.length < 2) { return 0; } int max = 0; int left = 0; int right = height.length - 1; while (left < right) { max = Math.max(max, (right - left) * Math.min(height[left], height[right])); if (height[left] < height[right]) left++; else right--; } return max; } 57 Count and Say 57.1 Problem The count-and-say sequence is the sequence of integers beginning as follows: 1, 11, 21, 1211, 111221, ... 1 is read off as "one 1" or 11. 11 is read off as "two 1s" or 21. 21 is read off as "one 2, then one 1" or 1211. Given an integer n, generate the nth sequence. 57.2 Java Solution The problem can be solved by using a simple iteration. See Java solution below: public String countAndSay(int n) { if (n <= 0) return null; String result = "1"; 99 | 181

100. int i = 1; while (i < n) { StringBuilder sb = new StringBuilder(); int count = 1; for (int j = 1; j < result.length(); j++) { if (result.charAt(j) == result.charAt(j - 1)) { count++; } else { sb.append(count); sb.append(result.charAt(j - 1)); count = 1; } } sb.append(count); sb.append(result.charAt(result.length() - 1)); result = sb.toString(); i++; } return result; } 58 Repeated DNA Sequences 58.1 Problem All DNA is composed of a series of nucleotides abbreviated as A, C, G, and T, for example: "ACGAATTCCG". When studying DNA, it is sometimes useful to identify repeated sequences within the DNA. Write a function to find all the 10-letter-long sequences (substrings) that occur more than once in a DNA molecule. For example, given s = "AAAAACCCCCAAAAACCCCCCAAAAAGGGTTT", re- turn: ["AAAAACCCCC", "CCCCCAAAAA"]. 58.2 Java Solution The key to solve this problem is that each of the 4 nucleotides can be stored in 2 bits. So the 10-letter-long sequence can be converted to 20-bits-long integer. The following is a Java solution. You may use an example to manually execute the program and see how it works. 100 | 181

101.public List<String> findRepeatedDnaSequences(String s) { List<String> result = new ArrayList<String>(); int len = s.length(); if (len < 10) { return result; } Map<Character, Integer> map = new HashMap<Character, Integer>(); map.put(’A’, 0); map.put(’C’, 1); map.put(’G’, 2); map.put(’T’, 3); Set<Integer> temp = new HashSet<Integer>(); Set<Integer> added = new HashSet<Integer>(); int hash = 0; for (int i = 0; i < len; i++) { if (i < 9) { //each ACGT fit 2 bits, so left shift 2 hash = (hash << 2) + map.get(s.charAt(i)); } else { hash = (hash << 2) + map.get(s.charAt(i)); //make length of hash to be 20 hash = hash & (1 << 20) - 1; if (temp.contains(hash) && !added.contains(hash)) { result.add(s.substring(i - 9, i + 1)); added.add(hash); //track added } else { temp.add(hash); } } } return result; } 59 Add Two Numbers The problem: 101 | 181

102.59 Add Two Numbers You are given two linked lists representing two non-negative numbers. The digits are stored in reverse order and each of their nodes contain a single digit. Add the two numbers and return it as a linked list. Input: (2 ->4 ->3) + (5 ->6 ->4) Output: 7 ->0 ->8 59.1 Thoughts This is a simple problem. It can be solved by doing the following: • Use a flag to mark if previous sum is >= 10 • Handle the situation that one list is longer than the other • Correctly move the 3 pointers p1, p2 and p3 which pointer to two input lists and one output list This leads to solution 1. 59.2 Solution 1 // Definition for singly-linked list. public class ListNode { int val; ListNode next; ListNode(int x) { val = x; next = null; } } public class Solution { public ListNode addTwoNumbers(ListNode l1, ListNode l2) { ListNode p1 = l1; ListNode p2 = l2; ListNode newHead = new ListNode(0); ListNode p3 = newHead; int val;//store sum boolean flag = false;//flag if greater than 10 while(p1 != null || p2 != null){ //both p1 and p2 have value if(p1 != null && p2 != null){ if(flag){ val = p1.val + p2.val + 1; }else{ 102 | 181 Program Creek

103. 59 Add Two Numbers val = p1.val + p2.val; } //if sum >= 10 if(val >= 10 ){ flag = true; //if sum < 10 }else{ flag = false; } p3.next = new ListNode(val%10); p1 = p1.next; p2 = p2.next; //p1 is null, because p2 is longer }else if(p2 != null){ if(flag){ val = p2.val + 1; if(val >= 10){ flag = true; }else{ flag = false; } }else{ val = p2.val; flag = false; } p3.next = new ListNode(val%10); p2 = p2.next; ////p2 is null, because p1 is longer }else if(p1 != null){ if(flag){ val = p1.val + 1; if(val >= 10){ flag = true; }else{ flag = false; } }else{ val = p1.val; flag = false; } p3.next = new ListNode(val%10); p1 = p1.next; Program Creek 103 | 181

104.59 Add Two Numbers } p3 = p3.next; } //handle situation that same length final sum >=10 if(p1 == null && p2 == null && flag){ p3.next = new ListNode(1); } return newHead.next; } } The hard part is how to make the code more readable. Adding some internal com- ments and refactoring some code are helpful. 59.3 Solution 2 There is nothing wrong with solution 1, but the code is not readable. We can refactor the code and make it much shorter and cleaner. public class Solution { public ListNode addTwoNumbers(ListNode l1, ListNode l2) { int carry =0; ListNode newHead = new ListNode(0); ListNode p1 = l1, p2 = l2, p3=newHead; while(p1 != null || p2 != null){ if(p1 != null){ carry += p1.val; p1 = p1.next; } if(p2 != null){ carry += p2.val; p2 = p2.next; } p3.next = new ListNode(carry%10); p3 = p3.next; carry /= 10; } if(carry==1) p3.next=new ListNode(1); return newHead.next; 104 | 181 Program Creek

105. } } Exactly the same thing! 59.4 Quesion What is the digits are stored in regular order instead of reversed order? Answer: We can simple reverse the list, calculate the result, and reverse the result. 60 Reorder List The problem: Given a singly linked list L: L0→L1→ ... →Ln-1→Ln, reorder it to: L0→Ln→L1→Ln- 1→L2→Ln-2→... For example, given 1,2,3,4, reorder it to 1,4,2,3. You must do this in-place without altering the nodes’ values. 60.1 Thoughts This problem is not straightforward, because it requires "in-place" operations. That means we can only change their pointers, not creating a new list. 60.2 Solution This problem can be solved by doing the following: • Break list in the middle to two lists (use fast & slow pointers) • Reverse the order of the second list • Merge two list back together The following code is a complete runnable class with testing. //Class definition of ListNode class ListNode { int val; ListNode next; ListNode(int x) { val = x; next = null; } 105 | 181

106.60 Reorder List } public class ReorderList { public static void main(String[] args) { ListNode n1 = new ListNode(1); ListNode n2 = new ListNode(2); ListNode n3 = new ListNode(3); ListNode n4 = new ListNode(4); n1.next = n2; n2.next = n3; n3.next = n4; printList(n1); reorderList(n1); printList(n1); } public static void reorderList(ListNode head) { if (head != null && head.next != null) { ListNode slow = head; ListNode fast = head; //use a fast and slow pointer to break the link to two parts. while (fast != null && fast.next != null && fast.next.next!= null) { //why need third/second condition? System.out.println("pre "+slow.val + " " + fast.val); slow = slow.next; fast = fast.next.next; System.out.println("after " + slow.val + " " + fast.val); } ListNode second = slow.next; slow.next = null;// need to close first part // now should have two lists: head and fast // reverse order for second part second = reverseOrder(second); ListNode p1 = head; ListNode p2 = second; //merge two lists here while (p2 != null) { ListNode temp1 = p1.next; 106 | 181 Program Creek

107. 60 Reorder List ListNode temp2 = p2.next; p1.next = p2; p2.next = temp1; p1 = temp1; p2 = temp2; } } } public static ListNode reverseOrder(ListNode head) { if (head == null || head.next == null) { return head; } ListNode pre = head; ListNode curr = head.next; while (curr != null) { ListNode temp = curr.next; curr.next = pre; pre = curr; curr = temp; } // set head node’s next head.next = null; return pre; } public static void printList(ListNode n) { System.out.println("------"); while (n != null) { System.out.print(n.val); n = n.next; } System.out.println(); } } 60.3 Takeaway Messages from This Problem The three steps can be used to solve other problems of linked list. A little diagram may help better understand them. Reverse List: Program Creek 107 | 181

108. Merge List: 108 | 181

109. 61 Linked List Cycle 61 Linked List Cycle Leetcode Problem: Linked List Cycle Given a linked list, determine if it has a cycle in it. 61.1 Naive Approach class ListNode { int val; Program Creek 109 | 181

110.61 Linked List Cycle ListNode next; ListNode(int x) { val = x; next = null; } } public class Solution { public boolean hasCycle(ListNode head) { ListNode p = head; if(head == null) return false; if(p.next == null) return false; while(p.next != null){ if(head == p.next){ return true; } p = p.next; } return false; } } Result: Submission Result: Time Limit Exceeded Last executed input: 3,2,0,-4, tail connects to node index 1 61.2 Accepted Approach Use fast and low pointer. The advantage about fast/slow pointers is that when a circle is located, the fast one will catch the slow one for sure. 110 | 181 Program Creek

111.public class Solution { public boolean hasCycle(ListNode head) { ListNode fast = head; ListNode slow = head; if(head == null) return false; if(head.next == null) return false; while(fast != null && fast.next != null){ slow = slow.next; fast = fast.next.next; if(slow == fast) return true; } return false; } } 62 Copy List with Random Pointer Problem: A linked list is given such that each node contains an additional random pointer which could point to any node in the list or null. Return a deep copy of the list. 62.1 Some Thoughts We can solve this problem by doing the following steps: • copy every node, i.e., duplicate every node, and insert it to the list • copy random pointers for all newly created nodes • break the list to two 62.2 First Attempt (Wrong) What is wrong with the following code? 111 | 181

112.62 Copy List with Random Pointer /** * Definition for singly-linked list with a random pointer. * class RandomListNode { * int label; * RandomListNode next, random; * RandomListNode(int x) { this.label = x; } * }; */ public class Solution { public RandomListNode copyRandomList(RandomListNode head) { if(head == null) return null; RandomListNode p = head; //copy every node and insert to list while(p != null){ RandomListNode copy = new RandomListNode(p.label); copy.next = p.next; p.next = copy; p = copy.next; } //copy random pointer for each new node p = head; while(p != null){ p.next.random = p.random.next;//p.random can be null, so need null checking here! p = p.next.next; } //break list to two p = head; while(p != null){ p.next = p.next.next; p = p.next;//point to the wrong node now! } return head.next; } } The code above seems totally fine. It follows the steps designed. But it has run-time errors. Why? The problem is in the parts of copying random pointer and breaking list. 112 | 181 Program Creek

113. 62 Copy List with Random Pointer 62.3 Correct Solution public RandomListNode copyRandomList(RandomListNode head) { if (head == null) return null; RandomListNode p = head; // copy every node and insert to list while (p != null) { RandomListNode copy = new RandomListNode(p.label); copy.next = p.next; p.next = copy; p = copy.next; } // copy random pointer for each new node p = head; while (p != null) { if (p.random != null) p.next.random = p.random.next; p = p.next.next; } // break list to two p = head; RandomListNode newHead = head.next; while (p != null) { RandomListNode temp = p.next; p.next = temp.next; if (temp.next != null) temp.next = temp.next.next; p = p.next; } return newHead; } The break list part above move pointer 2 steps each time, you can also move one at a time which is simpler, like the following: while(p != null && p.next != null){ RandomListNode temp = p.next; p.next = temp.next; p = temp; } Program Creek 113 | 181

115.63.1 Key to solve this problem The key to solve the problem is defining a fake head. Then compare the first elements from each list. Add the smaller one to the merged list. Finally, when one of them is empty, simply append it to the merged list, since it is already sorted. 63.2 Java Solution /** * Definition for singly-linked list. * public class ListNode { * int val; * ListNode next; * ListNode(int x) { * val = x; * next = null; * } * } */ public class Solution { public ListNode mergeTwoLists(ListNode l1, ListNode l2) { ListNode p1 = l1; ListNode p2 = l2; ListNode fakeHead = new ListNode(0); ListNode p = fakeHead; while(p1 != null && p2 != null){ if(p1.val <= p2.val){ p.next = p1; p1 = p1.next; }else{ p.next = p2; p2 = p2.next; } p = p.next; } if(p1 != null) p.next = p1; if(p2 != null) p.next = p2; return fakeHead.next; } } 115 | 181

116.64 Merge k Sorted Lists 64 Merge k Sorted Lists Merge k sorted linked lists and return it as one sorted list. Analyze and describe its complexity. 64.1 Thoughts The simplest solution is using PriorityQueue. The elements of the priority queue are ordered according to their natural ordering, or by a comparator provided at the construction time (in this case). 64.2 Java Solution import java.util.ArrayList; import java.util.Comparator; import java.util.PriorityQueue; // Definition for singly-linked list. class ListNode { int val; ListNode next; ListNode(int x) { val = x; next = null; } } public class Solution { public ListNode mergeKLists(ArrayList<ListNode> lists) { if (lists.size() == 0) return null; //PriorityQueue is a sorted queue PriorityQueue<ListNode> q = new PriorityQueue<ListNode>(lists.size(), new Comparator<ListNode>() { public int compare(ListNode a, ListNode b) { if (a.val > b.val) return 1; else if(a.val == b.val) return 0; else 116 | 181 Program Creek

117. return -1; } }); //add first node of each list to the queue for (ListNode list : lists) { if (list != null) q.add(list); } ListNode head = new ListNode(0); ListNode p = head; // serve as a pointer/cursor while (q.size() > 0) { ListNode temp = q.poll(); //poll() retrieves and removes the head of the queue - q. p.next = temp; //keep adding next element of each list if (temp.next != null) q.add(temp.next); p = p.next; } return head.next; } } Time: log(k) * n. k is number of list and n is number of total elements. 65 Remove Duplicates from Sorted List Given a sorted linked list, delete all duplicates such that each element appear only once. For example, Given 1->1->2, return 1->2. Given 1->1->2->3->3, return 1->2->3. 65.1 Thoughts The key of this problem is using the right loop condition. And change what is nec- essary in each loop. You can use different iteration conditions like the following 2 117 | 181

119. while( p!= null && p.next != null){ if(p.val == p.next.val){ p.next = p.next.next; }else{ p = p.next; } } return head; } } 66 Partition List Given a linked list and a value x, partition it such that all nodes less than x come before nodes greater than or equal to x. You should preserve the original relative order of the nodes in each of the two partitions. For example, Given 1->4->3->2->5->2 and x = 3, return 1->2->2->4->3->5. 66.1 Naive Solution (Wrong) The following is a solution I write at the beginning. It contains a trivial problem, but it took me a long time to fix it. /** * Definition for singly-linked list. * public class ListNode { * int val; * ListNode next; * ListNode(int x) { * val = x; * next = null; * } * } */ public class Solution { public ListNode partition(ListNode head, int x) { if(head == null) return null; ListNode fakeHead1 = new ListNode(0); ListNode fakeHead2 = new ListNode(0); 119 | 181

121. p = prev.next; p2 = p2.next; } } // close the list p2.next = null; prev.next = fakeHead2.next; return fakeHead1.next; } } 67 LRU Cache 67.1 Problem Design and implement a data structure for Least Recently Used (LRU) cache. It should support the following operations: get and set. get(key) - Get the value (will always be positive) of the key if the key exists in the cache, otherwise return -1. set(key, value) - Set or insert the value if the key is not already present. When the cache reached its capacity, it should invalidate the least recently used item before inserting a new item. 67.2 Java Solution The key to solve this problem is using a double linked list which enables us to quickly move nodes. 121 | 181

124. public int key; public DoubleLinkedListNode pre; public DoubleLinkedListNode next; public DoubleLinkedListNode(int key, int value) { val = value; this.key = key; } } 68 Intersection of Two Linked Lists 68.1 Problem Write a program to find the node at which the intersection of two singly linked lists begins. For example, the following two linked lists: A: a1 -> a2 -> c1 -> c2 -> c3 -> B: b1 -> b2 -> b3 begin to intersect at node c1. 68.2 Java Solution First calculate the length of two lists and find the difference. Then start from the longer list at the diff offset, iterate though 2 lists and find the node. /** * Definition for singly-linked list. * public class ListNode { * int val; * ListNode next; * ListNode(int x) { * val = x; * next = null; * } * } */ public class Solution { 124 | 181

125. public ListNode getIntersectionNode(ListNode headA, ListNode headB) { int len1 = 0; int len2 = 0; ListNode p1=headA, p2=headB; if (p1 == null || p2 == null) return null; while(p1 != null){ len1++; p1 = p1.next; } while(p2 !=null){ len2++; p2 = p2.next; } int diff = 0; p1=headA; p2=headB; if(len1 > len2){ diff = len1-len2; int i=0; while(i<diff){ p1 = p1.next; i++; } }else{ diff = len2-len1; int i=0; while(i<diff){ p2 = p2.next; i++; } } while(p1 != null && p2 != null){ if(p1.val == p2.val){ return p1; }else{ } p1 = p1.next; p2 = p2.next; } return null; } } 125 | 181

126.69 Java PriorityQueue Class Example 69 Java PriorityQueue Class Example In Java, the PriorityQueue class is implemented as a priority heap. Heap is an impor- tant data structure in computer science. For a quick overview of heap, here is a very good tutorial. 69.1 Simple Example The following examples shows the basic operations of PriorityQueue such as offer(), peek(), poll(), and size(). import java.util.Comparator; import java.util.PriorityQueue; public class PriorityQueueTest { static class PQsort implements Comparator<Integer> { public int compare(Integer one, Integer two) { return two - one; } } public static void main(String[] args) { int[] ia = { 1, 10, 5, 3, 4, 7, 6, 9, 8 }; PriorityQueue<Integer> pq1 = new PriorityQueue<Integer>(); // use offer() method to add elements to the PriorityQueue pq1 for (int x : ia) { pq1.offer(x); } System.out.println("pq1: " + pq1); PQsort pqs = new PQsort(); PriorityQueue<Integer> pq2 = new PriorityQueue<Integer>(10, pqs); // In this particular case, we can simply use Collections.reverseOrder() // instead of self-defined comparator for (int x : ia) { pq2.offer(x); } System.out.println("pq2: " + pq2); 126 | 181 Program Creek

127. // print size System.out.println("size: " + pq2.size()); // return highest priority element in the queue without removing it System.out.println("peek: " + pq2.peek()); // print size System.out.println("size: " + pq2.size()); // return highest priority element and removes it from the queue System.out.println("poll: " + pq2.poll()); // print size System.out.println("size: " + pq2.size()); System.out.print("pq2: " + pq2); } } Output: pq1: [1, 3, 5, 8, 4, 7, 6, 10, 9] pq2: [10, 9, 7, 8, 3, 5, 6, 1, 4] size: 9 peek: 10 size: 9 poll: 10 size: 8 pq2: [9, 8, 7, 4, 3, 5, 6, 1] 69.2 Example of Solving Problems Using PriorityQueue Merging k sorted list. For more details about PriorityQueue, please go to doc. 70 Solution for Binary Tree Preorder Traversal in Java Preorder binary tree traversal is a classic interview problem about trees. The key to solve this problem is to understand the following: • What is preorder? (parent node is processed before its children) • Use Stack from Java Core library It is not obvious what preorder for some strange cases. However, if you draw a stack and manually execute the program, how each element is pushed and popped is 127 | 181

128.obvious. The key to solve this problem is using a stack to store left and right children, and push right child first so that it is processed after the left child. public class TreeNode { int val; TreeNode left; TreeNode right; TreeNode(int x) { val = x; } } public class Solution { public ArrayList<Integer> preorderTraversal(TreeNode root) { ArrayList<Integer> returnList = new ArrayList<Integer>(); if(root == null) return returnList; Stack<TreeNode> stack = new Stack<TreeNode>(); stack.push(root); while(!stack.empty()){ TreeNode n = stack.pop(); returnList.add(n.val); if(n.right != null){ stack.push(n.right); } if(n.left != null){ stack.push(n.left); } } return returnList; } } 71 Solution of Binary Tree Inorder Traversal in Java The key to solve inorder traversal of binary tree includes the following: • The order of "inorder" is: left child ->parent ->right child 128 | 181

129. 71 Solution of Binary Tree Inorder Traversal in Java • Use a stack to track nodes • Understand when to push node into the stack and when to pop node out of the stack //Definition for binary tree public class TreeNode { int val; TreeNode left; TreeNode right; TreeNode(int x) { val = x; } } public class Solution { public ArrayList<Integer> inorderTraversal(TreeNode root) { // IMPORTANT: Please reset any member data you declared, as // the same Solution instance will be reused for each test case. ArrayList<Integer> lst = new ArrayList<Integer>(); if(root == null) return lst; Stack<TreeNode> stack = new Stack<TreeNode>(); //define a pointer to track nodes TreeNode p = root; while(!stack.empty() || p != null){ // if it is not null, push to stack //and go down the tree to left if(p != null){ stack.push(p); p = p.left; Program Creek 129 | 181

130. // if no left child // pop stack, process the node // then let p point to the right }else{ TreeNode t = stack.pop(); lst.add(t.val); p = t.right; } } return lst; } } 72 Solution of Iterative Binary Tree Postorder Traversal in Java The key to to iterative postorder traversal is the following: • The order of "Postorder" is: left child ->right child ->parent node. • Find the relation between the previously visited node and the current node • Use a stack to track nodes As we go down the tree, check the previously visited node. If it is the parent of the current node, we should add current node to stack. When there is no children for current node, pop it from stack. Then the previous node become to be under the current node for next loop. //Definition for binary tree public class TreeNode { int val; TreeNode left; TreeNode right; TreeNode(int x) { val = x; } } public class Solution { public ArrayList<Integer> postorderTraversal(TreeNode root) { ArrayList<Integer> lst = new ArrayList<Integer>(); if(root == null) 130 | 181

131. return lst; Stack<TreeNode> stack = new Stack<TreeNode>(); stack.push(root); TreeNode prev = null; while(!stack.empty()){ TreeNode curr = stack.peek(); // go down the tree. //check if current node is leaf, if so, process it and pop stack, //otherwise, keep going down if(prev == null || prev.left == curr || prev.right == curr){ //prev == null is the situation for the root node if(curr.left != null){ stack.push(curr.left); }else if(curr.right != null){ stack.push(curr.right); }else{ stack.pop(); lst.add(curr.val); } //go up the tree from left node //need to check if there is a right child //if yes, push it to stack //otherwise, process parent and pop stack }else if(curr.left == prev){ if(curr.right != null){ stack.push(curr.right); }else{ stack.pop(); lst.add(curr.val); } //go up the tree from right node //after coming back from right node, process parent node and pop stack. }else if(curr.right == prev){ stack.pop(); lst.add(curr.val); } prev = curr; } return lst; } } 131 | 181

132.73 Validate Binary Search Tree 73 Validate Binary Search Tree Problem: Given a binary tree, determine if it is a valid binary search tree (BST). Assume a BST is defined as follows: • The left subtree of a node contains only nodes with keys less than the node’s key. • The right subtree of a node contains only nodes with keys greater than the node’s key. • Both the left and right subtrees must also be binary search trees. 73.1 Thoughts about This Problem All values on the left sub tree must be less than root, and all values on the right sub tree must be greater than root. 73.2 Java Solution // Definition for binary tree class TreeNode { int val; TreeNode left; TreeNode right; TreeNode(int x) { val = x; } } public class Solution { public static boolean isValidBST(TreeNode root) { return validate(root, Integer.MIN_VALUE, Integer.MAX_VALUE); } public static boolean validate(TreeNode root, int min, int max) { if (root == null) { return true; } // not in range 132 | 181 Program Creek

133. if (root.val <= min || root.val >= max) { return false; } // left subtree must be < root.val && right subtree must be > root.val return validate(root.left, min, root.val) && validate(root.right, root.val, max); } } 74 Flatten Binary Tree to Linked List Given a binary tree, flatten it to a linked list in-place. For example, Given 1 / \ 2 5 / \ \ 3 4 6 The flattened tree should look like: 1 \ 2 \ 3 \ 4 \ 5 \ 6 74.1 Thoughts Go down through the left, when right is not null, push right to stack. 74.2 Java Solution 133 | 181

134./** * Definition for binary tree * public class TreeNode { * int val; * TreeNode left; * TreeNode right; * TreeNode(int x) { val = x; } * } */ public class Solution { public void flatten(TreeNode root) { Stack<TreeNode> stack = new Stack<TreeNode>(); TreeNode p = root; while(p != null || !stack.empty()){ if(p.right != null){ stack.push(p.right); } if(p.left != null){ p.right = p.left; p.left = null; }else if(!stack.empty()){ TreeNode temp = stack.pop(); p.right=temp; } p = p.right; } } } 75 Path Sum Given a binary tree and a sum, determine if the tree has a root-to-leaf path such that adding up all the values along the path equals the given sum. For example: Given the below binary tree and sum = 22, 5 / \ 4 8 / / \ 11 13 4 134 | 181

136. return false; } } 75.2 Java Solution 2 - Recursion public boolean hasPathSum(TreeNode root, int sum) { if (root == null) return false; if (root.val == sum && (root.left == null && root.right == null)) return true; return hasPathSum(root.left, sum - root.val) || hasPathSum(root.right, sum - root.val); } Thanks to nebulaliang, this solution is wonderful! 76 Construct Binary Tree from Inorder and Postorder Traversal Given inorder and postorder traversal of a tree, construct the binary tree. 76.1 Throughts This problem can be illustrated by using a simple example. in-order: 4 2 5 (1) 6 7 3 8 post-order: 4 5 2 6 7 8 3 (1) From the post-order array, we know that last element is the root. We can find the root in in-order array. Then we can identify the left and right sub-trees of the root from in-order array. Using the length of left sub-tree, we can identify left and right sub-trees in post-order array. Recursively, we can build up the tree. 76.2 Java Solution //Definition for binary tree 136 | 181

137.public class TreeNode { int val; TreeNode left; TreeNode right; TreeNode(int x) { val = x; } } public class Solution { public TreeNode buildTree(int[] inorder, int[] postorder) { int inStart = 0; int inEnd = inorder.length-1; int postStart =0; int postEnd = postorder.length-1; return buildTree(inorder, inStart, inEnd, postorder, postStart, postEnd); } public TreeNode buildTree(int[] inorder, int inStart, int inEnd, int[] postorder, int postStart, int postEnd){ if(inStart > inEnd || postStart > postEnd) return null; int rootValue = postorder[postEnd]; TreeNode root = new TreeNode(rootValue); int k=0; for(int i=0; i< inorder.length; i++){ if(inorder[i]==rootValue){ k = i; break; } } root.left = buildTree(inorder, inStart, k-1, postorder, postStart, postStart+k-(inStart+1)); // Becuase k is not the length, it it need to -(inStart+1) to get the length root.right = buildTree(inorder, k+1, inEnd, postorder, postStart+k-inStart, postEnd-1); // postStart+k-inStart = postStart+k-(inStart+1) +1 return root; } } 137 | 181

138.77 Convert Sorted Array to Binary Search Tree Given an array where elements are sorted in ascending order, convert it to a height balanced BST. 77.1 Thoughts Straightforward! Recursively do the job. 77.2 Java Solution // Definition for binary tree class TreeNode { int val; TreeNode left; TreeNode right; TreeNode(int x) { val = x; } } public class Solution { public TreeNode sortedArrayToBST(int[] num) { if (num.length == 0) return null; return sortedArrayToBST(num, 0, num.length - 1); } public TreeNode sortedArrayToBST(int[] num, int start, int end) { if (start > end) return null; int mid = (start + end) / 2; TreeNode root = new TreeNode(num[mid]); root.left = sortedArrayToBST(num, start, mid - 1); root.right = sortedArrayToBST(num, mid + 1, end); return root; } } 138 | 181

139. 78 Convert Sorted List to Binary Search Tree 78 Convert Sorted List to Binary Search Tree Given a singly linked list where elements are sorted in ascending order, convert it to a height balanced BST. 78.1 Thoughts If you are given an array, the problem is quite straightforward. But things get a little more complicated when you have a singly linked list instead of an array. Now you no longer have random access to an element in O(1) time. Therefore, you need to create nodes bottom-up, and assign them to its parents. The bottom-up approach enables us to access the list in its order at the same time as creating nodes. 78.2 Java Solution // Definition for singly-linked list. class ListNode { int val; ListNode next; ListNode(int x) { val = x; next = null; } } // Definition for binary tree class TreeNode { int val; TreeNode left; TreeNode right; TreeNode(int x) { val = x; } } public class Solution { static ListNode h; public TreeNode sortedListToBST(ListNode head) { if (head == null) return null; Program Creek 139 | 181

140. h = head; int len = getLength(head); return sortedListToBST(0, len - 1); } // get list length public int getLength(ListNode head) { int len = 0; ListNode p = head; while (p != null) { len++; p = p.next; } return len; } // build tree bottom-up public TreeNode sortedListToBST(int start, int end) { if (start > end) return null; // mid int mid = (start + end) / 2; TreeNode left = sortedListToBST(start, mid - 1); TreeNode root = new TreeNode(h.val); h = h.next; TreeNode right = sortedListToBST(mid + 1, end); root.left = left; root.right = right; return root; } } 79 Minimum Depth of Binary Tree Given a binary tree, find its minimum depth. The minimum depth is the number of nodes along the shortest path from the root node down to the nearest leaf node. 140 | 181

142.} 80 Binary Tree Maximum Path Sum Given a binary tree, find the maximum path sum. The path may start and end at any node in the tree. For example: Given the below binary tree, 1 / \ 2 3 Return 6. 80.1 Thoughts 1) Recursively solve this problem 2) Get largest left sum and right sum 2) Compare to the stored maximum 80.2 Java Solution 1 // Definition for binary tree class TreeNode { int val; TreeNode left; TreeNode right; TreeNode(int x) { val = x; } } public class Solution { //store max value int max; public int maxPathSum(TreeNode root) { max = (root == null) ? 0 : root.val; findMax(root); return max; } 142 | 181

143. public int findMax(TreeNode node) { if (node == null) return 0; // recursively get sum of left and right path int left = Math.max(findMax(node.left), 0); int right = Math.max(findMax(node.right), 0); //update maximum here max = Math.max(node.val + left + right, max); // return sum of largest path of current node return node.val + Math.max(left, right); } } 80.3 Java Solution 2 We can also use an array to store value for recursive methods. public class Solution { public int maxPathSum(TreeNode root) { int max[] = new int[1]; max[0] = Integer.MIN_VALUE; calculateSum(root, max); return max[0]; } public int calculateSum(TreeNode root, int[] max) { if (root == null) return 0; int left = calculateSum(root.left, max); int right = calculateSum(root.right, max); int current = Math.max(root.val, Math.max(root.val + left, root.val + right)); max[0] = Math.max(max[0], Math.max(current, left + root.val + right)); return current; } } 143 | 181

144.81 Balanced Binary Tree 81 Balanced Binary Tree Given a binary tree, determine if it is height-balanced. For this problem, a height-balanced binary tree is defined as a binary tree in which the depth of the two subtrees of every node never differ by more than 1. 81.1 Thoughts A typical recursive problem for solving tree problems. 81.2 Java Solution // Definition for binary tree class TreeNode { int val; TreeNode left; TreeNode right; TreeNode(int x) { val = x; } } public class Solution { public boolean isBalanced(TreeNode root) { if (root == null) return true; if (getHeight(root) == -1) return false; return true; } public int getHeight(TreeNode root) { if (root == null) return 0; int left = getHeight(root.left); int right = getHeight(root.right); if (left == -1 || right == -1) return -1; if (Math.abs(left - right) > 1) { return -1; 144 | 181 Program Creek

145. } return Math.max(left, right) + 1; } } 82 Symmetric Tree 82.1 Problem Given a binary tree, check whether it is a mirror of itself (ie, symmetric around its center). For example, this binary tree is symmetric: 1 / \ 2 2 / \ / \ 3 4 4 3 But the following is not: 1 / \ 2 2 \ \ 3 3 82.2 Java Solution - Recursion This problem can be solve by using a simple recursion. The key is finding the con- ditions that return false, such as value is not equal, only one node(left or right) has value. public boolean isSymmetric(TreeNode root) { if (root == null) return true; return isSymmetric(root.left, root.right); } public boolean isSymmetric(TreeNode l, TreeNode r) { 145 | 181

146. if (l == null && r == null) { return true; } else if (r == null || l == null) { return false; } if (l.val != r.val) return false; if (!isSymmetric(l.left, r.right)) return false; if (!isSymmetric(l.right, r.left)) return false; return true; } 83 Clone Graph Java LeetCode Problem: Clone an undirected graph. Each node in the graph contains a label and a list of its neighbors. 146 | 181

147. 83 Clone Graph Java 83.1 Key to Solve This Problem • A queue is used to do breath first traversal. • a map is used to store the visited nodes. It is the map between original node and copied node. It would be helpful if you draw a diagram and visualize the problem. Program Creek 147 | 181

148.83 Clone Graph Java /** * Definition for undirected graph. * class UndirectedGraphNode { * int label; * ArrayList<UndirectedGraphNode> neighbors; * UndirectedGraphNode(int x) { label = x; neighbors = new ArrayList<UndirectedGraphNode>(); } * }; */ public class Solution { public UndirectedGraphNode cloneGraph(UndirectedGraphNode node) { if(node == null) return null; LinkedList<UndirectedGraphNode> queue = new LinkedList<UndirectedGraphNode>(); HashMap<UndirectedGraphNode, UndirectedGraphNode> map = new HashMap<UndirectedGraphNode,UndirectedGraphNode>(); UndirectedGraphNode newHead = new UndirectedGraphNode(node.label); queue.add(node); map.put(node, newHead); 148 | 181 Program Creek

149. while(!queue.isEmpty()){ UndirectedGraphNode curr = queue.pop(); ArrayList<UndirectedGraphNode> currNeighbors = curr.neighbors; for(UndirectedGraphNode aNeighbor: currNeighbors){ if(!map.containsKey(aNeighbor)){ UndirectedGraphNode copy = new UndirectedGraphNode(aNeighbor.label); map.put(aNeighbor,copy); map.get(curr).neighbors.add(copy); queue.add(aNeighbor); }else{ map.get(curr).neighbors.add(map.get(aNeighbor)); } } } return newHead; } } 84 How Developers Sort in Java? While analyzing source code of a large number of open source Java projects, I found Java developers frequently sort in two ways. One is using the sort() method of Col- lections or Arrays, and the other is using sorted data structures, such as TreeMap and TreeSet. 84.1 Using sort() Method If it is a collection, use Collections.sort() method. // Collections.sort List<ObjectName> list = new ArrayList<ObjectName>(); Collections.sort(list, new Comparator<ObjectName>() { public int compare(ObjectName o1, ObjectName o2) { return o1.toString().compareTo(o2.toString()); } }); If it is an array, use Arrays.sort() method. 149 | 181

150.84 How Developers Sort in Java? // Arrays.sort ObjectName[] arr = new ObjectName[10]; Arrays.sort(arr, new Comparator<ObjectName>() { public int compare(ObjectName o1, ObjectName o2) { return o1.toString().compareTo(o2.toString()); } }); This is very convenient if a collection or an array is already set up. 84.2 Using Sorted Data Structures If it is a list or set, use TreeSet to sort. // TreeSet Set<ObjectName> sortedSet = new TreeSet<ObjectName>(new Comparator<ObjectName>() { public int compare(ObjectName o1, ObjectName o2) { return o1.toString().compareTo(o2.toString()); } }); sortedSet.addAll(unsortedSet); If it is a map, use TreeMap to sort. TreeMap is sorted by key. // TreeMap - using String.CASE_INSENSITIVE_ORDER which is a Comparator that orders Strings by compareToIgnoreCase Map<String, Integer> sortedMap = new TreeMap<String, Integer>(String.CASE_INSENSITIVE_ORDER); sortedMap.putAll(unsortedMap); //TreeMap - In general, defined comparator Map<ObjectName, String> sortedMap = new TreeMap<ObjectName, String>(new Comparator<ObjectName>() { public int compare(ObjectName o1, ObjectName o2) { return o1.toString().compareTo(o2.toString()); } }); sortedMap.putAll(unsortedMap); This approach is very useful, if you would do a lot of search operations for the collection. The sorted data structure will give time complexity of O(logn), which is lower than O(n). 84.3 Bad Practices There are still bad practices, such as using self-defined sorting algorithm. Take the code below for example, not only the algorithm is not efficient, but also it is not 150 | 181 Program Creek

151.readable. This happens a lot in different forms of variations. double t; for (int i = 0; i < 2; i++) for (int j = i + 1; j < 3; j++) if (r[j] < r[i]) { t = r[i]; r[i] = r[j]; r[j] = t; } 85 Solution Merge Sort LinkedList in Java LeetCode - Sort List: Sort a linked list in O(n log n) time using constant space complexity. 85.1 Keys for solving the problem • Break the list to two in the middle • Recursively sort the two sub lists • Merge the two sub lists This is my accepted answer for the problem. package algorithm.sort; class ListNode { int val; ListNode next; ListNode(int x) { val = x; next = null; } } public class SortLinkedList { // merge sort public static ListNode mergeSortList(ListNode head) { if (head == null || head.next == null) return head; 151 | 181

152.85 Solution Merge Sort LinkedList in Java // count total number of elements int count = 0; ListNode p = head; while (p != null) { count++; p = p.next; } // break up to two list int middle = count / 2; ListNode l = head, r = null; ListNode p2 = head; int countHalf = 0; while (p2 != null) { countHalf++; ListNode next = p2.next; if (countHalf == middle) { p2.next = null; r = next; } p2 = next; } // now we have two parts l and r, recursively sort them ListNode h1 = mergeSortList(l); ListNode h2 = mergeSortList(r); // merge together ListNode merged = merge(h1, h2); return merged; } public static ListNode merge(ListNode l, ListNode r) { ListNode p1 = l; ListNode p2 = r; ListNode fakeHead = new ListNode(100); ListNode pNew = fakeHead; while (p1 != null || p2 != null) { if (p1 == null) { pNew.next = new ListNode(p2.val); p2 = p2.next; pNew = pNew.next; } else if (p2 == null) { 152 | 181 Program Creek

153. 85 Solution Merge Sort LinkedList in Java pNew.next = new ListNode(p1.val); p1 = p1.next; pNew = pNew.next; } else { if (p1.val < p2.val) { // if(fakeHead) pNew.next = new ListNode(p1.val); p1 = p1.next; pNew = pNew.next; } else if (p1.val == p2.val) { pNew.next = new ListNode(p1.val); pNew.next.next = new ListNode(p1.val); pNew = pNew.next.next; p1 = p1.next; p2 = p2.next; } else { pNew.next = new ListNode(p2.val); p2 = p2.next; pNew = pNew.next; } } } // printList(fakeHead.next); return fakeHead.next; } public static void main(String[] args) { ListNode n1 = new ListNode(2); ListNode n2 = new ListNode(3); ListNode n3 = new ListNode(4); ListNode n4 = new ListNode(3); ListNode n5 = new ListNode(4); ListNode n6 = new ListNode(5); n1.next = n2; n2.next = n3; n3.next = n4; n4.next = n5; n5.next = n6; n1 = mergeSortList(n1); printList(n1); } public static void printList(ListNode x) { if(x != null){ Program Creek 153 | 181

154. System.out.print(x.val + " "); while (x.next != null) { System.out.print(x.next.val + " "); x = x.next; } System.out.println(); } } } Output: 233445 86 Quicksort Array in Java Quicksort is a divide and conquer algorithm. It first divides a large list into two smaller sub-lists and then recursively sort the two sub-lists. If we want to sort an array without any extra space, Quicksort is a good option. On average, time complexity is O(n log(n)). The basic step of sorting an array are as follows: • Select a pivot, normally the middle one • From both ends, swap elements and make all elements on the left less than the pivot and all elements on the right greater than the pivot • Recursively sort left part and right part package algorithm.sort; public class QuickSort { public static void main(String[] args) { int[] x = { 9, 2, 4, 7, 3, 7, 10 }; printArray(x); int low = 0; int high = x.length - 1; quickSort(x, low, high); printArray(x); } public static void quickSort(int[] arr, int low, int high) { 154 | 181

155. if (arr == null || arr.length == 0) return; if (low >= high) return; //pick the pivot int middle = low + (high - low) / 2; int pivot = arr[middle]; //make left < pivot and right > pivot int i = low, j = high; while (i <= j) { while (arr[i] < pivot) { i++; } while (arr[j] > pivot) { j--; } if (i <= j) { int temp = arr[i]; arr[i] = arr[j]; arr[j] = temp; i++; j--; } } //recursively sort two sub parts if (low < j) quickSort(arr, low, j); if (high > i) quickSort(arr, i, high); } public static void printArray(int[] x) { for (int a : x) System.out.print(a + " "); System.out.println(); } } Output: 9 2 4 7 3 7 10 2 3 4 7 7 9 10 The mistake I made is selecting the middle element. The middle element is not (low+high)/2, but low + (high-low)/2. For other parts of the programs, just follow the 155 | 181

156.87 Solution Sort a linked list using insertion sort in Java algorithm. 87 Solution Sort a linked list using insertion sort in Java Insertion Sort List: Sort a linked list using insertion sort. This is my accepted answer for LeetCode problem - Sort a linked list using insertion sort in Java. It is a complete program. Before coding for that, here is an example of insertion sort from wiki. You can get an idea of what is insertion sort. Code: package algorithm.sort; class ListNode { int val; ListNode next; ListNode(int x) { val = x; next = null; } } public class SortLinkedList { public static ListNode insertionSortList(ListNode head) { 156 | 181 Program Creek

158. ListNode n6 = new ListNode(5); n1.next = n2; n2.next = n3; n3.next = n4; n4.next = n5; n5.next = n6; n1 = insertionSortList(n1); printList(n1); } public static void printList(ListNode x) { if(x != null){ System.out.print(x.val + " "); while (x.next != null) { System.out.print(x.next.val + " "); x = x.next; } System.out.println(); } } } Output: 233445 88 Maximum Gap 88.1 Problem Given an unsorted array, find the maximum difference between the successive ele- ments in its sorted form. Try to solve it in linear time/space. Return 0 if the array contains less than 2 ele- ments. You may assume all elements in the array are non-negative integers and fit in the 32-bit signed integer range. 88.2 Java Solution 1 - Sort A straightforward solution would be sorting the array first (O(nlogn) and then finding the maximum gap. The basic idea is to project each element of the array to an array of 158 | 181

159. 88 Maximum Gap buckets. Each bucket tracks the maximum and minimum elements. Finally, scanning the bucket list, we can get the maximum gap. The key part is to get the interval: From: interval * (num[i] - min) = 0 and interval * (max -num[i]) = n interval = num.length / (max - min) See the internal comment for more details. 88.3 Java Solution 2 - Bucket Sort We can use a bucket-sort like algorithm to solve this problem in time of O(n) and space O(n). class Bucket{ int low; int high; public Bucket(){ low = -1; high = -1; } } public int maximumGap(int[] num) { if(num == null || num.length < 2){ return 0; } int max = num[0]; int min = num[0]; for(int i=1; i<num.length; i++){ max = Math.max(max, num[i]); min = Math.min(min, num[i]); } // initialize an array of buckets Bucket[] buckets = new Bucket[num.length+1]; //project to (0 - n) for(int i=0; i<buckets.length; i++){ buckets[i] = new Bucket(); } double interval = (double) num.length / (max - min); //distribute every number to a bucket array for(int i=0; i<num.length; i++){ int index = (int) ((num[i] - min) * interval); if(buckets[index].low == -1){ buckets[index].low = num[i]; buckets[index].high = num[i]; Program Creek 159 | 181

160. }else{ buckets[index].low = Math.min(buckets[index].low, num[i]); buckets[index].high = Math.max(buckets[index].high, num[i]); } } //scan buckets to find maximum gap int result = 0; int prev = buckets[0].high; for(int i=1; i<buckets.length; i++){ if(buckets[i].low != -1){ result = Math.max(result, buckets[i].low-prev); prev = buckets[i].high; } } return result; } 89 Iteration vs. Recursion in Java 89.1 Recursion Consider the factorial function: n!=n*(n-1)*(n-2)*...*1 There are many ways to compute factorials. One way is that n! is equal to n*(n-1)!. Therefore the program can be directly written as: Program 1: int factorial (int n) { if (n == 1) { return 1; } else { return n*factorial(n-1); } } In order to run this program, the computer needs to build up a chain of multipli- cations: factorial(n) → factorial(n-1) → factorial(n-2) → ... → factorial(1). Therefore, the computer has to keep track of the multiplications to be performed later on. This type of program, characterized by a chain of operations, is called recursion. Recursion can be further categorized into linear and tree recursion. When the amount of infor- mation needed to keep track of the chain of operations grows linearly with the input, 160 | 181

161. 89 Iteration vs. Recursion in Java the recursion is called linear recursion. The computation of n! is such a case, because the time required grows linearly with n. Another type of recursion, tree recursion, happens when the amount of information grows exponentially with the input. But we will leave it undiscussed here and go back shortly afterwards. 89.2 Iteration A different perspective on computing factorials is by first multiplying 1 by 2, then multiplying the result by 3, then by 4, and so on until n. More formally, the program can use a counter that counts from 1 up to n and compute the product simultaneously until the counter exceeds n. Therefore the program can be written as: Program 2: int factorial (int n) { int product = 1; for(int i=2; i<n; i++) { product *= i; } return product; } This program, by contrast to program 2, does not build a chain of multiplication. At each step, the computer only need to keep track of the current values of the product and i. This type of program is called iteration, whose state can be summarized by a fixed number of variables, a fixed rule that describes how the variables should be updated, and an end test that specifies conditions under which the process should terminate. Same as recursion, when the time required grows linearly with the input, we call the iteration linear recursion. 89.3 Recursion vs Iteration Compared the two processes, we can find that they seem almost same, especially in term of mathematical function. They both require a number of steps proportional to n to compute n!. On the other hand, when we consider the running processes of the two programs, they evolve quite differently. In the iterative case, the program variables provide a complete description of the state. If we stopped the computation in the middle, to resume it only need to supply the computer with all variables. However, in the recursive process, information is maintained by the computer, therefore "hidden" to the program. This makes it almost impossible to resume the program after stopping it. 89.4 Tree recursion As described above, tree recursion happens when the amount of information grows exponentially with the input. For instance, consider the sequence of Fibonacci num- Program Creek 161 | 181

162.89 Iteration vs. Recursion in Java bers defined as follows: By the definition, Fibonacci numbers have the following sequence, where each num- ber is the sum of the previous two: 0, 1, 1, 2, 3, 5, 8, 13, 21, ... A recursive program can be immediately written as: Program 3: int fib (int n) { if (n == 0) { return 0; } else if (n == 1) { return 1; } else { return fib(n-1) + fib(n-2); } } Therefore, to compute fib(5), the program computes fib(4) and fib(3). To computer fib(4), it computes fib(3) and fib(2). Notice that the fib procedure calls itself twice at the last line. Two observations can be obtained from the definition and the program: • The ith Fibonacci number Fib(i) is equal to phi(i)/rootsquare(5) rounded to the nearest integer, which indicates that Fibonacci numbers grow exponentially. • This is a bad way to compute Fibonacci numbers because it does redundant computation. Computing the running time of this procedure is beyond the scope of this article, but one can easily find that in books of algorithms, which is O(phi(n)). Thus, the program takes an amount of time that grows exponentially with the input. On the other hand, we can also write the program in an iterative way for computing the Fibonacci numbers. Program 4 is a linear iteration. The difference in time required by Program 3 and 4 is enormous, even for small inputs. Program 4: int fib (int n) { int fib = 0; int a = 1; for(int i=0; i<n; i++) { fib = fib + a; a = fib; } return fib; } 162 | 181 Program Creek

163. However, one should not think tree-recursive programs are useless. When we con- sider programs that operate on hierarchically data structures rather than numbers, tree-recursion is a natural and powerful tool. It can help us understand and design programs. Compared with Program 3 and 4, we can easily tell Program 3 is more straightforward, even if less efficient. After that, we can most likely reformulate the program into an iterative way. 90 Edit Distance in Java From Wiki: In computer science, edit distance is a way of quantifying how dissimilar two strings (e.g., words) are to one another by counting the minimum number of operations required to transform one string into the other. There are three operations permitted on a word: replace, delete, insert. For example, the edit distance between "a" and "b" is 1, the edit distance between "abc" and "def" is 3. This post analyzes how to calculate edit distance by using dynamic programming. 90.1 Key Analysis Let dp[i][j] stands for the edit distance between two strings with length i and j, i.e., word1[0,...,i-1] and word2[0,...,j-1]. There is a relation between dp[i][j] and dp[i-1][j-1]. Let’s say we transform from one string to another. The first string has length i and it’s last character is "x"; the second string has length j and its last character is "y". The following diagram shows the relation. 163 | 181

164.90 Edit Distance in Java • if x == y, then dp[i][j] == dp[i-1][j-1] • if x != y, and we insert y for word1, then dp[i][j] = dp[i][j-1] + 1 • if x != y, and we delete x for word1, then dp[i][j] = dp[i-1][j] + 1 • if x != y, and we replace x with y for word1, then dp[i][j] = dp[i-1][j-1] + 1 • When x!=y, dp[i][j] is the min of the three situations. Initial condition: dp[i][0] = i, dp[0][j] = j 90.2 Java Code After the analysis above, the code is just a representation of it. public static int minDistance(String word1, String word2) { int len1 = word1.length(); int len2 = word2.length(); // len1+1, len2+1, because finally return dp[len1][len2] int[][] dp = new int[len1 + 1][len2 + 1]; for (int i = 0; i <= len1; i++) { dp[i][0] = i; } for (int j = 0; j <= len2; j++) { dp[0][j] = j; } 164 | 181 Program Creek

165. //iterate though, and check last char for (int i = 0; i < len1; i++) { char c1 = word1.charAt(i); for (int j = 0; j < len2; j++) { char c2 = word2.charAt(j); //if last two chars equal if (c1 == c2) { //update dp value for +1 length dp[i + 1][j + 1] = dp[i][j]; } else { int replace = dp[i][j] + 1; int insert = dp[i][j + 1] + 1; int delete = dp[i + 1][j] + 1; int min = replace > insert ? insert : replace; min = delete > min ? min : delete; dp[i + 1][j + 1] = min; } } } return dp[len1][len2]; } 91 Single Number The problem: Given an array of integers, every element appears twice except for one. Find that single one. 91.1 Thoughts The key to solve this problem is bit manipulation. XOR will return 1 only on two different bits. So if two numbers are the same, XOR will return 0. Finally only one number left. 91.2 Java Solution public class Solution { public int singleNumber(int[] A) { 165 | 181

166. int x=0; for(int a: A){ x = x ^ a; } return x; } } The question now is do you know any other ways to do this? 92 Single Number II 92.1 Problem Given an array of integers, every element appears three times except for one. Find that single one. 92.2 Java Solution This problem is similar to Single Number. public int singleNumber(int[] A) { int ones = 0, twos = 0, threes = 0; for (int i = 0; i < A.length; i++) { twos |= ones & A[i]; ones ^= A[i]; threes = ones & twos; ones &= ~threes; twos &= ~threes; } return ones; } 93 Twitter Codility Problem Max Binary Gap Problem: Get maximum binary Gap. 166 | 181

167. For example, 9’s binary form is 1001, the gap is 2. 93.1 Thoughts The key to solve this problem is the fact that an integer x & 1 will get the last digit of the integer. 93.2 Java Solution public class Solution { public static int solution(int N) { int max = 0; int count = -1; int r = 0; while (N > 0) { // get right most bit & shift right r = N & 1; N = N >> 1; if (0 == r && count >= 0) { count++; } if (1 == r) { max = count > max ? count : max; count = 0; } } return max; } public static void main(String[] args) { System.out.println(solution(9)); } } 94 Number of 1 Bits 167 | 181

168.94.1 Problem Write a function that takes an unsigned integer and returns the number of ’1’ bits it has (also known as the Hamming weight). For example, the 32-bit integer ’11’ has binary representation 00000000000000000000000000001011, so the function should return 3. 94.2 Java Solution public int hammingWeight(int n) { int count = 0; for(int i=1; i<33; i++){ if(getBit(n, i) == true){ count++; } } return count; } public boolean getBit(int n, int i){ return (n & (1 << i)) != 0; } 95 Reverse Bits 95.1 Problem Reverse bits of a given 32 bits unsigned integer. For example, given input 43261596 (represented in binary as 00000010100101000001111010011100), return 964176192 (represented in binary as 00111001011110000010100101000000). Follow up: If this function is called many times, how would you optimize it? Related problem: Reverse Integer 95.2 Java Solution public int reverseBits(int n) { for (int i = 0; i < 16; i++) { n = swapBits(n, i, 32 - i - 1); } 168 | 181

169. return n; } public int swapBits(int n, int i, int j) { int a = (n >> i) & 1; int b = (n >> j) & 1; if ((a ^ b) != 0) { return n ^= (1 << i) | (1 << j); } return n; } 96 Permutations Given a collection of numbers, return all possible permutations. For example, [1,2,3] have the following permutations: [1,2,3], [1,3,2], [2,1,3], [2,3,1], [3,1,2], and [3,2,1]. 96.1 Java Solution 1 We can get all permutations by the following steps: [1] [2, 1] [1, 2] [3, 2, 1] [2, 3, 1] [2, 1, 3] [3, 1, 2] [1, 3, 2] [1, 2, 3] Loop through the array, in each iteration, a new number is added to different loca- tions of results of previous iteration. Start from an empty List. public ArrayList<ArrayList<Integer>> permute(int[] num) { ArrayList<ArrayList<Integer>> result = new ArrayList<ArrayList<Integer>>(); //start from an empty list 169 | 181

170.96 Permutations result.add(new ArrayList<Integer>()); for (int i = 0; i < num.length; i++) { //list of list in current iteration of the array num ArrayList<ArrayList<Integer>> current = new ArrayList<ArrayList<Integer>>(); for (ArrayList<Integer> l : result) { // # of locations to insert is largest index + 1 for (int j = 0; j < l.size()+1; j++) { // + add num[i] to different locations l.add(j, num[i]); ArrayList<Integer> temp = new ArrayList<Integer>(l); current.add(temp); //System.out.println(temp); // - remove num[i] add l.remove(j); } } result = new ArrayList<ArrayList<Integer>>(current); } return result; } 96.2 Java Solution 2 We can also recursively solve this problem. Swap each element with each element after it. public ArrayList<ArrayList<Integer>> permute(int[] num) { ArrayList<ArrayList<Integer>> result = new ArrayList<ArrayList<Integer>>(); permute(num, 0, result); return result; } void permute(int[] num, int start, ArrayList<ArrayList<Integer>> result) { if (start >= num.length) { ArrayList<Integer> item = convertArrayToList(num); result.add(item); } for (int j = start; j <= num.length - 1; j++) { 170 | 181 Program Creek

171. swap(num, start, j); permute(num, start + 1, result); swap(num, start, j); } } private ArrayList<Integer> convertArrayToList(int[] num) { ArrayList<Integer> item = new ArrayList<Integer>(); for (int h = 0; h < num.length; h++) { item.add(num[h]); } return item; } private void swap(int[] a, int i, int j) { int temp = a[i]; a[i] = a[j]; a[j] = temp; } 97 Permutations II Given a collection of numbers that might contain duplicates, return all possible unique permutations. For example, [1,1,2] have the following unique permutations: [1,1,2], [1,2,1], and [2,1,1]. 97.1 Basic Idea For each number in the array, swap it with every element after it. To avoid duplicate, we need to check the existing sequence first. 97.2 Java Solution 1 public ArrayList<ArrayList<Integer>> permuteUnique(int[] num) { ArrayList<ArrayList<Integer>> result = new ArrayList<ArrayList<Integer>>(); permuteUnique(num, 0, result); return result; } 171 | 181

172.97 Permutations II private void permuteUnique(int[] num, int start, ArrayList<ArrayList<Integer>> result) { if (start >= num.length ) { ArrayList<Integer> item = convertArrayToList(num); result.add(item); } for (int j = start; j <= num.length-1; j++) { if (containsDuplicate(num, start, j)) { swap(num, start, j); permuteUnique(num, start + 1, result); swap(num, start, j); } } } private ArrayList<Integer> convertArrayToList(int[] num) { ArrayList<Integer> item = new ArrayList<Integer>(); for (int h = 0; h < num.length; h++) { item.add(num[h]); } return item; } private boolean containsDuplicate(int[] arr, int start, int end) { for (int i = start; i <= end-1; i++) { if (arr[i] == arr[end]) { return false; } } return true; } private void swap(int[] a, int i, int j) { int temp = a[i]; a[i] = a[j]; a[j] = temp; } 97.3 Java Solution 2 Use set to maintain uniqueness: public static ArrayList<ArrayList<Integer>> permuteUnique(int[] num) { ArrayList<ArrayList<Integer>> returnList = new ArrayList<ArrayList<Integer>>(); returnList.add(new ArrayList<Integer>()); 172 | 181 Program Creek

173. for (int i = 0; i < num.length; i++) { Set<ArrayList<Integer>> currentSet = new HashSet<ArrayList<Integer>>(); for (List<Integer> l : returnList) { for (int j = 0; j < l.size() + 1; j++) { l.add(j, num[i]); ArrayList<Integer> T = new ArrayList<Integer>(l); l.remove(j); currentSet.add(T); } } returnList = new ArrayList<ArrayList<Integer>>(currentSet); } return returnList; } Thanks to Milan for such a simple solution! 98 Permutation Sequence The set [1,2,3,. . . ,n] contains a total of n! unique permutations. By listing and labeling all of the permutations in order, We get the following se- quence (ie, for n = 3): "123" "132" "213" "231" "312" "321" Given n and k, return the kth permutation sequence. Note: Given n will be between 1 and 9 inclusive. 98.1 Thoughts Naively loop through all cases will not work. 98.2 Java Solution 1 public class Solution { public String getPermutation(int n, int k) { // initialize all numbers 173 | 181

174.98 Permutation Sequence ArrayList<Integer> numberList = new ArrayList<Integer>(); for (int i = 1; i <= n; i++) { numberList.add(i); } // change k to be index k--; // set factorial of n int mod = 1; for (int i = 1; i <= n; i++) { mod = mod * i; } String result = ""; // find sequence for (int i = 0; i < n; i++) { mod = mod / (n - i); // find the right number(curIndex) of int curIndex = k / mod; // update k k = k % mod; // get number according to curIndex result += numberList.get(curIndex); // remove from list numberList.remove(curIndex); } return result.toString(); } } 98.3 Java Solution 2 public class Solution { public String getPermutation(int n, int k) { boolean[] output = new boolean[n]; StringBuilder buf = new StringBuilder(""); int[] res = new int[n]; res[0] = 1; for (int i = 1; i < n; i++) res[i] = res[i - 1] * i; for (int i = n - 1; i >= 0; i--) { 174 | 181 Program Creek

175. int s = 1; while (k > res[i]) { s++; k = k - res[i]; } for (int j = 0; j < n; j++) { if (j + 1 <= s && output[j]) { s++; } } output[s - 1] = true; buf.append(Integer.toString(s)); } return buf.toString(); } } 99 Generate Parentheses Given n pairs of parentheses, write a function to generate all combinations of well- formed parentheses. For example, given n = 3, a solution set is: "((()))", "(()())", "(())()", "()(())", "()()()" 99.1 Java Solution Read the following solution, give n=2, walk though the code. Hopefully you will quickly get an idea. public List<String> generateParenthesis(int n) { ArrayList<String> result = new ArrayList<String>(); ArrayList<Integer> diff = new ArrayList<Integer>(); result.add(""); diff.add(0); for (int i = 0; i < 2 * n; i++) { ArrayList<String> temp1 = new ArrayList<String>(); 175 | 181

176. ArrayList<Integer> temp2 = new ArrayList<Integer>(); for (int j = 0; j < result.size(); j++) { String s = result.get(j); int k = diff.get(j); if (i < 2 * n - 1) { temp1.add(s + "("); temp2.add(k + 1); } if (k > 0 && i < 2 * n - 1 || k == 1 && i == 2 * n - 1) { temp1.add(s + ")"); temp2.add(k - 1); } } result = new ArrayList<String>(temp1); diff = new ArrayList<Integer>(temp2); } return result; } Solution is provided first now. I will come back and draw a diagram to explain the solution. 100 Reverse Integer LeetCode - Reverse Integer: Reverse digits of an integer. Example1: x = 123, return 321 Example2: x = -123, return -321 100.1 Naive Method We can convert the integer to a string/char array, reverse the order, and convert the string/char array back to an integer. However, this will require extra space for the string. It doesn’t seem to be the right way, if you come with such a solution. 100.2 Efficient Approach Actually, this can be done by using the following code. public int reverse(int x) { 176 | 181

177. 100 Reverse Integer //flag marks if x is negative boolean flag = false; if (x < 0) { x = 0 - x; flag = true; } int res = 0; int p = x; while (p > 0) { int mod = p % 10; p = p / 10; res = res * 10 + mod; } if (flag) { res = 0 - res; } return res; } 100.3 Succinct Solution This solution is from Sherry, it is succinct and it is pretty. public int reverse(int x) { int rev = 0; while(x != 0){ rev = rev*10 + x%10; x = x/10; } return rev; } 100.4 Handle Out of Range Problem As we form a new integer, it is possible that the number is out of range. We can use the following code to assign the newly formed integer. When it is out of range, throw an exception. try{ result = ...; }catch(InputMismatchException exception){ System.out.println("This is not an integer"); Program Creek 177 | 181

178.} Please leave your comment if there is any better solutions. 101 Palindrome Number Determine whether an integer is a palindrome. Do this without extra space. 101.1 Thoughts Problems related with numbers are frequently solved by / and Note: no extra space here means do not convert the integer to string, since string will be a copy of the integer and take extra space. The space take by div, left, and right can be ignored. 101.2 Java Solution public class Solution { public boolean isPalindrome(int x) { //negative numbers are not palindrome if (x < 0) return false; // initialize how many zeros int div = 1; while (x / div >= 10) { div *= 10; } while (x != 0) { int left = x / div; int right = x % 10; if (left != right) return false; x = (x % div) / 10; div /= 100; } return true; } } 178 | 181

179. 102 Pow(x, n) 102 Pow(x, n) Problem: Implement pow(x, n). This is a great example to illustrate how to solve a problem during a technical in- terview. The first and second solution exceeds time limit; the third and fourth are accepted. 102.1 Naive Method First of all, assuming n is not negative, to calculate x to the power of n, we can simply multiply x n times, i.e., x * x * ... * x. The time complexity is O(n). The implementation is as simple as: public class Solution { public double pow(double x, int n) { if(x == 0) return 0; if(n == 0) return 1; double result=1; for(int i=1; i<=n; i++){ result = result * x; } return result; } } Now we should think about how to do better than O(n). 102.2 Recursive Method Naturally, we next may think how to do it in O(logn). We have a relation that xnˆ = ˆ x(n/2) * xˆ(n/2) * xˆ(n public static double pow(double x, int n) { if(n == 0) return 1; if(n == 1) return x; Program Creek 179 | 181

180.102 Pow(x, n) int half = n/2; int remainder = n%2; if(n % 2 ==1 && x < 0 && n < 0) return - 1/(pow(-x, half) * pow(-x, half) * pow(-x, remainder)); else if (n < 0) return 1/(pow(x, -half) * pow(x, -half) * pow(x, -remainder)); else return (pow(x, half) * pow(x, half) * pow(x, remainder)); } In this solution, we can handle cases that x <0 and n <0. This solution actually takes more time than the first solution. Why? 102.3 Accepted Solution The accepted solution is also recursive, but does division first. Time complexity is O(nlog(n)). The key part of solving this problem is the while loop. public double pow(double x, int n) { if (n == 0) return 1; if (n == 1) return x; int pn = n > 0 ? n : -n;// positive n int pn2 = pn; double px = x > 0 ? x : -x;// positive x double result = px; int k = 1; //the key part of solving this problem while (pn / 2 > 0) { result = result * result; pn = pn / 2; k = k * 2; } result = result * pow(px, pn2 - k); // handle negative result if (x < 0 && n % 2 == 1) result = -result; // handle negative power if (n < 0) result = 1 / result; 180 | 181 Program Creek

181. 102 Pow(x, n) return result; } 102.4 Best Solution The most understandable solution I have found so far. public double power(double x, int n) { if (n == 0) return 1; double v = power(x, n / 2); if (n % 2 == 0) { return v * v; } else { return v * v * x; } } public double pow(double x, int n) { if (n < 0) { return 1 / power(x, -n); } else { return power(x, n); } } Program Creek 181 | 181

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