本专题是关于组织和处理信息的基本数据结构和算法,在本章中就抽象数据类型,数据的堆栈和队列进行了介绍,了解何时以及为何使用特定数据,以及他们之间的区别。

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1.CSE 373 : Data Structures and Algorithms Lecture 1: Introduction; ADTs; Stacks/Queues Riley Porter Winter 2017 Winter 2017 CSE373: Data Structures and Algorithms 1

2.Welcome! This course is about fundamental data structures and algorithms for organizing and processing information “ Classic ” data structures / algorithms and how to analyze rigorously their efficiency and when to use them Queues, dictionaries, graphs, sorting, etc. Today in class: Introductions and course mechanics What this course is about Start abstract d ata t ypes (ADTs), stacks , and queues Winter 2017 CSE373: Data Structures and Algorithms 2

3.Overloading Go to : Overload Form link: https:// catalyst.uw.edu / webq /survey/ cseadv / 321439 or http://tinyurl.com/ hz9sxzd Code Word: Given in Lecture Do this by 1 hour after this lecture! Winter 2017 CSE373: Data Structures and Algorithms 3

4.Course staff Instructor : Riley Porter , CSE 450, rileymp2@ cs.washington.edu Winter 2017 CSE373: Data Structures and Algorithms 4 TA : Zelina Chen : zelinac@cs.washington.edu TA: Paul Curry : paulmc@cs.washington.edu TA: Josh Curtis : curtijd@cs.washington.edu TA: Chloe Lathe : lathec@cs.washington.edu TA: Trung Ly : trungly@cs.washington.edu TA: Matthew Rockett : rockettm@cs.washington.edu TA: Kyle Thayer : kthayer@cs.washington.edu TA: Raquel Van Hofwegen : raqvh@cs.washington.edu TA: Pascale Wallace Patterson : pattersp@cs.washington.edu TA: Rebecca Yuen : rebyuen@cs.washington.edu TA: Hunter Zahn : hzahn93@cs.washington.edu

5.Concise to-do list In next 24-48 hours: Take homework 0 (worth 0 points) as Catalyst quiz Read /skim Chapters 1 and 3 of Weiss book Relevant to Homework 1, due next week Set up your Java environment for Homework 1 Check out the Course Website and read all the course policies: https://courses.cs.washington.edu/courses/cse373/17wi Winter 2017 CSE373: Data Structures and Algorithms 5

6.Communication Course email list : cse373_wi17@ u.washington.edu Students and staff already subscribed Fairly low traffic Course staff: cse373-staff@ cs.washington.edu plus individual emails Discussion b oard For appropriate discussions; TAs will monitor Encouraged, but won’t use for important announcements Anonymous feedback link For good and bad: if you don’t tell me, I don’t know Winter 2017 CSE373: Data Structures and Algorithms 6

7.Course meetings Lectures Materials posted on the website Ask questions, focus on key ideas Not recorded Sections on Thursdays Programming practice, homework prep M ath review and example exam problems Would be a really bad idea not to go, won’t always post all materials on the website Winter 2017 CSE373: Data Structures and Algorithms 7

8.Office Hours Riley: Tuesday 1: 3 0 – 3:20pm in CSE 450 Use them: please visit me... I have candy... Ideally not just for homework questions (but that’s OK too) TA’s: To be determined – will be posted on the website today/tomorrow Winter 2017 CSE373: Data Structures and Algorithms 8

9.Course materials All lecture and section materials will be posted But they are visual aids, not always a complete description! If you have to miss, find out what you missed Textbook: Weiss 3 rd Edition in Java Good read, but only responsible for lecture/ hw topics 3 rd edition improves on 2 nd , but we’ll support the 2 nd A good Java reference of your choosing Google is only so helpful Winter 2017 CSE373: Data Structures and Algorithms 9

10.Computing College of Arts & Sciences Instructional Computing Lab http://depts.washington.edu/aslab / Communications building Or your own machine Will use Java 8 for the programming assignments Eclipse is recommended programming environment Winter 2017 CSE373: Data Structures and Algorithms 10

11.Course Work ~6 homework assingments (50%) Most involve programming, but also written questions Higher-level concepts than “just code it up” First programming assignment released soon and due next week Midterm (20%): TBD. Will announce more about this in the coming week. Final (30%): Tuesday, March 14 th , 2:30-4:20 Winter 2017 CSE373: Data Structures and Algorithms 11

12.Collaboration and Academic Integrity Read the course policy very carefully Explains quite clearly how you can and cannot get/provide help on homework and projects Always explain any unconventional action on your part When it happens, when you submit, not when asked Honest work is the most important feature of a university Winter 2017 CSE373: Data Structures and Algorithms 12

13.Academic Honesty D etails You are expected to do your own work Exceptions (group work), if any, will be clearly announced Sharing solutions, doing work for, or accepting work from others is cheating Referring to solutions from this or other courses from previous quarters is cheating But you can learn from each other: see the policy Winter 2017 CSE373: Data Structures and Algorithms 13

14.Moar Academic Honesty Winter 2017 CSE373: Data Structures and Algorithms 14 You spend at least 30 minutes on each and every problem (or programming assignment) alone, before discussing it with others. Cooperation is limited to group discussion and brainstorming. No written or electronic material may be exchanged or leave the brainstorming session. You write up each and every problem in your own writing, using your own words, and fully understanding your solution (similarly you must write code on your own). You identify each person that you collaborated with at the top of your written homework or in your README file.

15.What 373 is about Deeply understand the basic structures used in all software Understand the data structures and their trade-offs Rigorously analyze the algorithms that use them (math!) Learn how to pick “the right thing for the job” More thorough and rigorous take on topics introduced in CSE143 (plus more new topics ) Practice design, analysis, and implementation The elegant interplay of “theory” and “engineering” at the core of computer science More programming experience (as a way to learn) Winter 2017 CSE373: Data Structures and Algorithms 15

16.Goals Be able to make good design choices as a developer, project manager, etc. Reason in terms of the general abstractions that come up in all non-trivial software (and many non-software) systems Be able to justify and communicate your design decisions Dan Grossman’s take: Key abstractions used almost every day in just about anything related to computing and software It is a vocabulary you are likely to internalize permanently Winter 2017 CSE373: Data Structures and Algorithms 16

17.In CSE 143 (Assumed Background) Fundamentals of computer science and object oriented programming Variables, conditionals, loops, methods, fundamentals of defining classes and inheritance, arrays, single linked lists, simple binary trees, recursion, some sorting and searching algorithms, basic algorithm analysis (e.g., O (n) vs O (n 2 ) and similar things ) What other data structures were in 143? Winter 2017 CSE373: Data Structures and Algorithms 17

18.143 vs 373 143: Showed you how to use data structures (be the Client vs the Implementor ) 373 : Provide you with the tools to understand when and why one would use certain data structures/algorithms over others And to be able to implement your own ! problem solving and thinking critically Winter 2017 CSE373: Data Structures and Algorithms 18

19.Topics Outline Introduction to Algorithm Analysis Lists, Stacks, Queues Trees, Hashing, Dictionaries Heaps, Priority Queues Sorting Disjoint Sets Graph Algorithms May have time for other brief exposure to topics, maybe parallelism, technical i nterview q uestions, dynamic programming Winter 2017 CSE373: Data Structures and Algorithms 19

20.Terminology Abstract Data Type (ADT) Mathematical description of a “thing” with set of operations Algorithm A high level, language-independent description of a step-by-step process Data structure A specific organization of data and family of algorithms for implementing an ADT Implementation of a data structure A specific implementation in a specific language Winter 2017 CSE373: Data Structures and Algorithms 20

21.Data structures (Often highly non-obvious ) ways to organize information to enable efficient computation over that information A data structure supports certain operations , each with a: Meaning: what does the operation do/return Performance: how efficient is the operation Examples: List with operations insert and delete Stack with operations push and pop Winter 2017 CSE373: Data Structures and Algorithms 21

22.Trade-offs A data structure strives to provide many useful, efficient operations But there are unavoidable trade-offs: Time vs. space One operation more efficient if another less efficient Generality vs. simplicity vs. performance We ask ourselves questions like: Does this support the operations I need efficiently? Will it be easy to use, implement, and debug? What assumptions am I making about how my software will be used? (E.g., more lookups or more inserts?) Winter 2017 CSE373: Data Structures and Algorithms 22

23.Array vs Linked List Array: May waste unneeded space or run out of space Space per element excellent Constant -time access to k th element For operation insertAtPosition , must shift all later elements Winter 2017 CSE373: Data Structures and Algorithms 23 List: Always just enough space Slightly more space per element No constant-time access to k th element For operation insertAtPosition must traverse all earlier elements

24.ADT vs. Data Structure vs. Implementation “Real life” Example (not perfect) ADT: Automobile Operations: Accelerate, decelerate, etc… Data Structure: Type of automobile Car, Motorcycle, Truck, etc… Implementation (of Car): 2009 Honda Civic, 2001 Subaru Outback, … Winter 2017 CSE373: Data Structures and Algorithms 24

25.Example: Stacks The Stack ADT supports operations: isEmpty : have there been same number of pops as pushes push : takes an item pop : raises an error if empty, else returns most-recently pushed item not yet returned by a pop … (possibly more operations) A Stack data structure could use a linked-list or an array or something else, and associated algorithms for the operations One implementation is in the library java.util.Stack Winter 2017 CSE373: Data Structures and Algorithms 25

26.The Stack ADT Operations: create destroy push pop top is_empty Can also be implemented with an array or a linked list This is Homework 1 (released soon, due next week) Type of elements is irrelevant Winter 2017 CSE373: Data Structures and Algorithms 26 A B C D E F E D C B A F

27.Why Stack ADT is useful It arises all the time in programming (e.g., see Weiss 3.6.3) Recursive function calls Balancing symbols (parentheses) Evaluating postfix notation: 3 4 + 5 * Clever: Infix ((3+4) * 5) to postfix conversion (see text) We can code up a reusable library We can communicate in high-level terms “Use a stack and push numbers, popping for operators…” Rather than, “create a linked list and add a node when…” Winter 2017 CSE373: Data Structures and Algorithms 27

28.The Queue ADT Operations create destroy enqueue dequeue is_empty Just like a stack except: Stack: LIFO (last-in-first-out) Queue: FIFO (first-in-first-out) Just as useful and ubiquitous Winter 2017 CSE373: Data Structures and Algorithms 28 B C D E F dequeue enqueue A G Front Back

29.Circular Array Queue Data Structure What if queue is empty? Enqueue ? Dequeue ? What if array is full? How to test for empty? What is the complexity of the operations? Can you find the k th element in the queue? Winter 2017 CSE373: Data Structures and Algorithms 29 // Basic idea only! enqueue (x) { Q[back] = x; back = (back + 1) % size } // Basic idea only! dequeue () { x = Q[front]; front = (front + 1) % size; return x; } b c d e f Q: 0 size - 1 front back Considerations: