1 / 27

Come on down!

Come on down!. Take and fill out a survey Get a copy of lecture slides Please sit in the first 5 rows!. CSE 326: Data Structures Lecture #1 Introduction. Alon Halevy Spring Quarter 2001. Today’s Outline. Administrative Stuff Overview of 326 Survey Introduction to Complexity.

gmckenzie
Télécharger la présentation

Come on down!

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Come on down! • Take and fill out a survey • Get a copy of lecture slides • Please sit in the first 5 rows!

  2. CSE 326: Data StructuresLecture #1Introduction Alon Halevy Spring Quarter 2001

  3. Today’s Outline • Administrative Stuff • Overview of 326 • Survey • Introduction to Complexity

  4. Course Information • Instructor: Alon Halevy <alon@cs> Office hours: Wed. 4:30-5:30, 310 Sieg Hall • TA: Maya Rodrig <rodrig@cs> Office hours: Monday 1:30-2:30. Meet in Sieg 226B • TA (1/2) (and C++ expert): Nicholas Bone (bone@cs) • Sections are held in: BLD 392, EE1 031. • Text: Data Structures & Algorithm Analysis in C++, 2nd edition, by Mark Allen Weiss

  5. Course Policies • Several written homeworks • Due at the start of class on due date • Several programming projects • Projects turned in electronically before 11pm on due date • 10% penalty for 1 weekday late; afterward, NOT accepted • Work in teams only on explicit team projects • Grading • homework: 25% • projects: 25% • midterm: 20% • final: 30%

  6. Course Mechanics • 326 Web page: www/education/courses/326/01sp • 326 course directory: /cse/courses/cse326 • 326 mailing list: cse326@cs.washington.edu • subscribe to the mailing list using majordomo, see homepage • Course labs are 232 and 329 Sieg Hall • lab has NT machines w/X servers to access UNIX • All programming projects graded on UNIX/g++

  7. What is this Course About? Clever ways to organize information in order to enable efficient computation • What do we mean by clever? • What do we mean by efficient?

  8. Clever? Efficient? Insert Delete Find Merge Shortest Paths Union Lists, Stacks, Queues Heaps Binary Search Trees AVL Trees Hash Tables Graphs Disjoint Sets Data Structures Algorithms

  9. Graphics Used Everywhere! Theory AI Applications Systems Mastery of this material separates you from: • Perhaps the most important course in your CS curriculum! • Guaranteed non-obsolescence!

  10. Anecdote #1 • N2 “pretty print” routine nearly dooms major expert system project at AT&T • 10 MB data = 10 days (100 MIPS) • programmer was brilliant, but he skipped 326…

  11. Asymptotic Complexity Our notion of efficiency: How the running time of an algorithm scales with the size of its input • several ways to further refine: • worst case • average case • amortized over a series of runs

  12. The Apocalyptic Laptop Seth Lloyd, SCIENCE, 31 Aug 2000

  13. Big Bang Ultimate Laptop, 1 year 1 second 1000 MIPS, since Big Bang 1000 MIPS, 1 day

  14. Specific Goals of the Course • Become familiar with some of the fundamental data structures in computer science • Improve ability to solve problems abstractly • data structures are the building blocks • Improve ability to analyze your algorithms • prove correctness • gauge (and improve) time complexity • Become modestly skilled with the UNIX operating system (you’ll need this in upcoming courses)

  15. One Preliminary Hurdle • Recall what you learned in CSE 321 … • proofs by mathematical induction • proofs by contradiction • formulas for calculating sums and products of series • recursion Know Sec 1.1 – 1.4 of text by heart!

  16. A Second Hurdle • Unix Experience 1975 all over again! • Try to login, edit, create a Makefile, and compile your favorite “hello world” program right away • Programming Project #1 distributed Wednesday • Bring your questions and frustrations to Section on Thursday!

  17. A Third Hurdle: Templates class Set_of_ints { public: insert( int x ); boolean is_member( int x ); … } template <class Obj> class Set { public: insert( Obj x ); boolean is_member( Obj x ); … } Set <int> SomeNumbers; Set <char *> SomeWords;

  18. In Every Silver Lining, There’s a Big Dark Cloud– George Carlin • Templates were invented 12 years ago, and still no compiler correctly implements them! • Using templates with multiple source files tricky • See Course Web pages and TAs for best way • MAINTAINING SANITY RULE • Write/debug first without templates • Templatize as need • Keep it simple!

  19. Handy Libraries • From Weiss: vector < int > MySafeIntArray; vector < double > MySafeFloatArray; string MySafeString; • Like arrays and char*, but provide • bounds checking • memory management • STL (Standard Template Library) • most of CSE 326 in a box • don’t use (unless told); we’ll be rolling our own

  20. C++  Data Structures One of the all time great books in computer science: The Art of Computer Programming (1968-1973) by Donald Knuth Examples in assembly language (and English)! American Scientist says: in top 12 books of the CENTURY! Very little about C++ in class.

  21. Abstract Data Types • Abstract Data Type (ADT) • Mathematical description of an object and the set of operations on the object Data Types integer, array, pointers, … tradeoffs! • Algorithms • binary search, quicksort, …

  22. ADT Presentation Algorithm • Present an ADT • Motivate with some applications • Repeat until it’s time to move on: • develop a data structure and algorithms for the ADT • analyze its properties • efficiency • correctness • limitations • ease of programming • Contrast strengths and weaknesses

  23. First Example: Queue ADT • Queue operations • create • destroy • enqueue • dequeue • is_empty • Queue property: if x is enQed before y is enQed, then x will be deQed before y is deQed FIFO: First In First Out F E D C B dequeue enqueue G A

  24. Applications of the Q • Hold jobs for a printer • Store packets on network routers • Make waitlists fair • Breadth first search

  25. enqueue(Object x) { Q[back] = x ; back = (back + 1) % size } Circular Array Q Data Structure Q size - 1 0 b c d e f front back How test for empty list? How to find K-th element in the queue? What is complexity of these operations? Limitations of this structure? • dequeue() { • x = Q[front] ; • front = (front + 1) % size; • return x ; }

  26. Linked List Q Data Structure b c d e f front back enqueue(Object x) { back->next = new Node(x); back = back->next; } dequeue() { saved = front->data; temp = front; front = front->next; delete temp ; return saved;} What are tradeoffs? • simplicity • speed • robustness • memory usage

  27. To Do • Return your survey before leaving! • Sign up on the cse326 mailing list • Check out the web page • Log on to the PCs in course labs and access an instructional UNIX server • Read Chapters 1 and 2 in the book

More Related