1 / 12

Data Structures 資料結構

Data Structures 資料結構. Instructor: 顏嗣鈞 E-mail: yen@cc.ee.ntu.edu.tw Web: http://www.ee.ntu.edu.tw/~yen Time: 9:10-12:00 AM, Thursday Place: EE 106 Office hours: by appointment Class web page: http://www.ee.ntu.edu.tw/~yen/courses/ds03.html. PREREQUISITES.

cera
Download Presentation

Data Structures 資料結構

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. Data Structures 資料結構

  2. Instructor: 顏嗣鈞 E-mail: yen@cc.ee.ntu.edu.tw Web: http://www.ee.ntu.edu.tw/~yen Time: 9:10-12:00 AM, Thursday Place: EE 106 Office hours: by appointment Class web page: http://www.ee.ntu.edu.tw/~yen/courses/ds03.html

  3. PREREQUISITES Familiarity in PASCAL, C, C++, or JAVA.

  4. Required textbook: Data Structures & Algorithm Analysis in C++(2nd Ed.), Mark Weiss, Addison Wesley.

  5. TOPICS lPRELIMINARIES: Introduction.  Algorithm analysis. lABSTRACT DATA TYPES: Stacks. Queues. Lists. List operations. List representations. List traversals. Doubly linked lists. lTREES: Tree operations. Tree representations. Tree traversals. Threaded trees. Binary trees. AVL trees. 2-3 trees. B-trees. Red-black trees. Binomial trees. Splay trees, and more. lHASHING: Chaining. Open addressing. Collision handling. lPRIORITY QUEUES: Binary heaps. Binomial heaps. Fibonacci heaps. Min-max heaps. Leftist heaps. Skew heaps. lSORTING: Insertion sort. Selection sort. Quicksort. Heapsort. Mergesort. Shellsort. Lower bound of sorting. lDISJOINT SETS: Set operations. Set representations. Union-find. Path compression. lGRAPHS: Graph operations. Graph representations. Basic graph algorithms. lAMORTIZED ANALYSIS. Binomial heaps, Skew heaps. Fibonacci heaps. lADVANCED DATA STRUCTURES: Tries. Top-down splay trees, and more.

  6. Grading Homework + Prog. Assignments 25% Midterm exam.: 35% Final exam.: 40%

  7. Life Cycle of Software Development

  8. Goal • Learn to write efficient and elegant software • How to choose between two algorithms • Which to use? bubble-sort, insertion-sort, merge-sort • How to choose appropriate data structures • Which to use? array, vector, linked list, binary tree

  9. Why should you care? • Complex data structures and algorithms are used in every real program • Data compression uses trees: MP3, Gif, etc… • Networking uses graphs: Routers and telephone networks • Security uses complex math algorithms: GCD and large decimals • Operating systems use queues and stacks: Scheduling and recursion • Many problems can only be solved using complex data structures and algorithms

  10. What this course is NOT about • This course is not about C++ • Although we will use C++ to implement some of the concepts • This course is not about MATH • Although we will use math to formalize many of the concepts • Competency in both math and C++ is therefore welcomed. • C++: inheritance, overloading, overriding, files, linked-lists, multi-dimensional arrays • Math: polynomials, logarithms, inductive proofs, logic

  11. The Big Idea • Definition of Abstract Data Type • A collection of data along with specific operations that manipulate that data • Has nothing to do with a programming language! • Two fundamental goals of algorithm analysis • Correctness: Prove that a program works as expected • Efficiency: Characterize the run-time of an algorithm

  12. The Big Idea • Alternative goals of algorithm analysis • Characterize the amount of memory required • Characterize the size of a programs code • Characterize the readability of a program • Characterize the robustness of a program

More Related