1. Come up and say hello! 1

2. CPSC 221: Algorithms and Data Structures Lecture #0: Introduction Steve Wolfman 2009W1 2

3. Today’s Outline Administrative Cruft Overview of the Course Queues Stacks 3

4. Course Information Instructor: Steve Wolfman ICCS 239 wolf@cs.ubc.ca Office hours: see website (some every day M-F!) TAs: Dan Li, Ben Jones, Shervin Mohammadi Tari, Martin Lau, Vicki McEachern TA Office hours: TBA; see website Texts: Epp Discrete Mathematics, Koffman C++ 4

5. Course Policies No late work; may be flexible with advance notice iClickers marked for participation, but can miss ~1/6 th of exercises and still get 100% Programming projects (~4) due 9PM on due date Written homework (~3) due 5PM on due date Grading –class participation:5% –labs:5% –assignments:15% –midterm:30% –final:45% 5 Must pass the final & combo of labs/assignments to pass the course.

6. Collaboration READ the collaboration policy on the website. You have LOTS of freedom to collaborate! Use it to learn and have fun while doing it! Don’t violate the collaboration policy. There’s no point in doing so, and the penalties are so severe that just thinking about them causes babies to cry *. 6 *Almost anything causes babies to cry, actually, but the cheating penalties really are severe.

7. Course Mechanics 221 Web page: www.ugrad.cs.ubc.ca/~cs221 221 Vista site: www.vista.ubc.ca Laboratories are in ICCS X251 and X350 –lab has SunRay thin clients: use the “Linux” logon All programming projects graded on UNIX/g++ 7

8. What is a Data Structure? data structure - 8

9. Observation All programs manipulate data –programs process, store, display, gather –data can be information, numbers, images, sound Each program must decide how to store and manipulate data Choice influences program at every level –execution speed –memory requirements –maintenance (debugging, extending, etc.) 9

10. Goals of the Course Become familiar with some of the fundamental data structures and algorithms in computer science Improve ability to solve problems abstractly –data structures and algorithms are the building blocks Improve ability to analyze your algorithms –prove correctness –gauge, compare, and improve time and space complexity Become modestly skilled with C++ and UNIX, but this is largely on your own! 10

11. What is an Abstract Data Type? Abstract Data Type (ADT) - 1) An opportunity for an acronym 2) Mathematical description of an object and the set of operations on the object 11

12. Data Structures as Algorithms Algorithm –A high level, language independent description of a step-by-step process for solving a problem Data Structure –A set of algorithms which implement an ADT 12

13. Why so many data structures? Ideal data structure: fast, elegant, memory efficient Generates tensions: –time vs. space –performance vs. elegance –generality vs. simplicity –one operation’s performance vs. another’s “Dictionary” ADT –list –binary search tree –AVL tree –Splay tree –B tree –Red-Black tree –hash table –… 13

14. Code Implementation Theoretically –abstract base class describes ADT –inherited implementations implement data structures –can change data structures transparently (to client code) Practice –different implementations sometimes suggest different interfaces (generality vs. simplicity) –performance of a data structure may influence form of client code (time vs. space, one operation vs. another) 14

15. ADT Presentation Algorithm Present an ADT Motivate with some applications Repeat until browned entirely through –develop a data structure for the ADT –analyze its properties efficiency correctness limitations ease of programming Contrast data structure’s strengths and weaknesses –understand when to use each one 15

16. Queue ADT Queue operations –create –destroy –enqueue –dequeue –is_empty Queue property: if x is enqueued before y is enqueued, then x will be dequeued before y is dequeued. FIFO: First In First Out F E D C B enqueue dequeue GA 16

17. Applications of the Q Hold jobs for a printer Store packets on network routers Hold memory “freelists” Make waitlists fair Breadth first search 17

18. Abstract Q Example enqueue R enqueue O dequeue enqueue T enqueue A enqueue T dequeue enqueue E dequeue 18

19. Abstract Q Example enqueue R enqueue O dequeue enqueue T enqueue A enqueue T dequeue enqueue E dequeue 19 In order, what letters are dequeued? a.OATE b.ROTA c.OTAE d.None of these, but it can be determined from just the ADT. e.None of these, and it cannot be determined from just the ADT.

20. Circular Array Q Data Structure void enqueue(Object x) { Q[back] = x back = (back + 1) % size } Object dequeue() { x = Q[front] front = (front + 1) % size return x } bcdef Q 0 size - 1 frontback bool is_empty() { return (front == back) } bool is_full() { return front == (back + 1) % size } This is pseudocode. Do not correct my semicolons 20

21. Circular Array Q Example enqueue R enqueue O dequeue enqueue T enqueue A enqueue T dequeue enqueue E dequeue 21

22. Circular Array Q Example enqueue R enqueue O dequeue enqueue T enqueue A enqueue T dequeue enqueue E dequeue 22 What are the final contents of the array? a.RTE b.RTET c.TETA d.TE e.None of these

23. Linked List Q Data Structure bcdef frontback void enqueue(Object x) { if (is_empty()) front = back = new Node(x) else back->next = new Node(x) back = back->next } Object dequeue() { assert(!is_empty) return_data = front->data temp = front front = front->next delete temp return return_data } bool is_empty() { return front == null } 23

24. Linked List Q Data Structure bcdef frontback void enqueue(Object x) { if (is_empty()) front = back = new Node(x) else back->next = new Node(x) back = back->next } Object dequeue() { assert(!is_empty) return_data = front->data temp = front front = front->next delete temp return return_data } bool is_empty() { return front == null } 24 What’s with the red text?

25. Circular Array vs. Linked List 25

26. Stack ADT Stack operations –create –destroy –push –pop –top –is_empty Stack property: if x is pushed before y is pushed, then x will be popped after y is popped LIFO: Last In First Out A BCDEFBCDEF E D C B A F 26

27. Stacks in Practice Function call stack Removing recursion Balancing symbols (parentheses) Evaluating Reverse Polish Notation Depth first search 27

28. Array Stack Data Structure S 0 size - 1 fedcb void push(Object x) { assert(!is_full()) S[back] = x back++ } Object top() { assert(!is_empty()) return S[back - 1] } back Object pop() { assert(!is_empty()) back-- return S[back] } bool is_empty() { return back == 0 } bool is_full() { return back == size } 28

29. Linked List Stack Data Structure bcdef back void push(Object x) { temp = back back = new Node(x) back->next = temp } Object top() { assert(!is_empty()) return back->data } Object pop() { assert(!is_empty()) return_data = back->data temp = back back = back->next delete temp return return_data } bool is_empty() { return back == null } 29

30. Data structures you should already know (a bit) Arrays Linked lists Trees Queues Stacks 30

31. To Do Check out the web page and Vista Download and read over Lab 1 materials Begin working through Chapters P and 1 of Koffman and Wolfgang (C++ background) Read sections 4.5-4.7, 5, and 6 except 6.4 of Koffman and Wolfgang (linked lists, stacks, and queues) 31

32. Coming Up Asymptotic Analysis Programming Project 1 (solving mazes with stacks and queues) 32