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6.046J

Design and Analysis of Algorithms

Prof. Erik Demaine, Prof. Srini Devadas, Prof. Nancy Lynch | Spring 2015

Data Science, Analytics & Computer Technology Algorithms and Data Structures Networks and Security Computer Science Science & Math Mathematics Engineering Applied Mathematics

前往原始課程

CC BY-NC-SA 4.0

課程簡介

This is an intermediate algorithms course with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application. Topics include divide-and-conquer, randomization, dynamic programming, greedy algorithms, incremental improvement, complexity, and cryptography.

Course Information

Source	MIT 開放式課程
科系	Electrical Engineering and Computer Science
Language	English
影片數	34

課程影片 (34)

1. Course Overview, Interval Scheduling

1. Course Overview, Interval Scheduling

2. Divide & Conquer: Convex Hull, Median Finding

2. Divide & Conquer: Convex Hull, Median Finding

R1. Matrix Multiplication and the Master Theorem

R1. Matrix Multiplication and the Master Theorem

3. Divide & Conquer: FFT

3. Divide & Conquer: FFT

R2. 2-3 Trees and B-Trees

R2. 2-3 Trees and B-Trees

4. Divide & Conquer: van Emde Boas Trees

4. Divide & Conquer: van Emde Boas Trees

5. Amortization: Amortized Analysis

5. Amortization: Amortized Analysis

6. Randomization: Matrix Multiply, Quicksort

6. Randomization: Matrix Multiply, Quicksort

R4. Randomized Select and Randomized Quicksort

R4. Randomized Select and Randomized Quicksort

7. Randomization: Skip Lists

7. Randomization: Skip Lists

8. Randomization: Universal & Perfect Hashing

8. Randomization: Universal & Perfect Hashing

R5. Dynamic Programming

R5. Dynamic Programming

9. Augmentation: Range Trees

9. Augmentation: Range Trees

10. Dynamic Programming: Advanced DP

10. Dynamic Programming: Advanced DP

11. Dynamic Programming: All-Pairs Shortest Paths

11. Dynamic Programming: All-Pairs Shortest Paths

12. Greedy Algorithms: Minimum Spanning Tree

12. Greedy Algorithms: Minimum Spanning Tree

R6. Greedy Algorithms

R6. Greedy Algorithms

13. Incremental Improvement: Max Flow, Min Cut

13. Incremental Improvement: Max Flow, Min Cut

14. Incremental Improvement: Matching

14. Incremental Improvement: Matching

R7. Network Flow and Matching

R7. Network Flow and Matching

15. Linear Programming: LP, reductions, Simplex

15. Linear Programming: LP, reductions, Simplex

16. Complexity: P, NP, NP-completeness, Reductions

16. Complexity: P, NP, NP-completeness, Reductions

R8. NP-Complete Problems

R8. NP-Complete Problems

17. Complexity: Approximation Algorithms

17. Complexity: Approximation Algorithms

18. Complexity: Fixed-Parameter Algorithms

18. Complexity: Fixed-Parameter Algorithms

R9. Approximation Algorithms: Traveling Salesman Problem

R9. Approximation Algorithms: Traveling Salesman Problem

19. Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees

19. Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees

20. Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees

20. Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees

R10. Distributed Algorithms

R10. Distributed Algorithms

21. Cryptography: Hash Functions

21. Cryptography: Hash Functions

22. Cryptography: Encryption

22. Cryptography: Encryption

R11. Cryptography: More Primitives

R11. Cryptography: More Primitives

23. Cache-Oblivious Algorithms: Medians & Matrices

23. Cache-Oblivious Algorithms: Medians & Matrices

24. Cache-Oblivious Algorithms: Searching & Sorting

24. Cache-Oblivious Algorithms: Searching & Sorting