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Uedu Open / Design and Analysis of Algorithms
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.
課程資訊
來源MIT 開放式課程
科系Electrical Engineering and Computer Science
語言English
影片數34
課程影片 (34)
1
1. Course Overview, Interval Scheduling
1. Course Overview, Interval Scheduling
2
2. Divide & Conquer: Convex Hull, Median Finding
2. Divide & Conquer: Convex Hull, Median Finding
3
R1. Matrix Multiplication and the Master Theorem
R1. Matrix Multiplication and the Master Theorem
4
3. Divide & Conquer: FFT
3. Divide & Conquer: FFT
5
R2. 2-3 Trees and B-Trees
R2. 2-3 Trees and B-Trees
6
4. Divide & Conquer: van Emde Boas Trees
4. Divide & Conquer: van Emde Boas Trees
7
5. Amortization: Amortized Analysis
5. Amortization: Amortized Analysis
8
6. Randomization: Matrix Multiply, Quicksort
6. Randomization: Matrix Multiply, Quicksort
9
R4. Randomized Select and Randomized Quicksort
R4. Randomized Select and Randomized Quicksort
10
7. Randomization: Skip Lists
7. Randomization: Skip Lists
11
8. Randomization: Universal & Perfect Hashing
8. Randomization: Universal & Perfect Hashing
12
R5. Dynamic Programming
R5. Dynamic Programming
13
9. Augmentation: Range Trees
9. Augmentation: Range Trees
14
10. Dynamic Programming: Advanced DP
10. Dynamic Programming: Advanced DP
15
11. Dynamic Programming: All-Pairs Shortest Paths
11. Dynamic Programming: All-Pairs Shortest Paths
16
12. Greedy Algorithms: Minimum Spanning Tree
12. Greedy Algorithms: Minimum Spanning Tree
17
R6. Greedy Algorithms
R6. Greedy Algorithms
18
13. Incremental Improvement: Max Flow, Min Cut
13. Incremental Improvement: Max Flow, Min Cut
19
14. Incremental Improvement: Matching
14. Incremental Improvement: Matching
20
R7. Network Flow and Matching
R7. Network Flow and Matching
21
15. Linear Programming: LP, reductions, Simplex
15. Linear Programming: LP, reductions, Simplex
22
16. Complexity: P, NP, NP-completeness, Reductions
16. Complexity: P, NP, NP-completeness, Reductions
23
R8. NP-Complete Problems
R8. NP-Complete Problems
24
17. Complexity: Approximation Algorithms
17. Complexity: Approximation Algorithms
25
18. Complexity: Fixed-Parameter Algorithms
18. Complexity: Fixed-Parameter Algorithms
26
R9. Approximation Algorithms: Traveling Salesman Problem
R9. Approximation Algorithms: Traveling Salesman Problem
27
19. Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees
19. Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees
28
20. Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees
20. Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees
29
R10. Distributed Algorithms
R10. Distributed Algorithms
30
21. Cryptography: Hash Functions
21. Cryptography: Hash Functions
31
22. Cryptography: Encryption
22. Cryptography: Encryption
32
R11. Cryptography: More Primitives
R11. Cryptography: More Primitives
33
23. Cache-Oblivious Algorithms: Medians & Matrices
23. Cache-Oblivious Algorithms: Medians & Matrices
34
24. Cache-Oblivious Algorithms: Searching & Sorting
24. Cache-Oblivious Algorithms: Searching & Sorting