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Home » Applied Sciences » Computer Science » Design and Analysis of Algorithms (M-I-T) » R1. Matrix Multiplication and the Master Theorem (M-I-T)

R1. Matrix Multiplication and the Master Theorem (M-I-T)

Course:Design and Analysis of Algorithms (M-I-T)
Discipline:Applied Sciences
Institute:MIT
Instructor(s): Prof. Dr. Erik Demaine, Prof. Dr. Srini Devadas, Prof. Dr. Nancy Lynch
Level:Undergraduate

Design and Analysis of Algorithms (M-I-T)

1. Course Overview, Interval Scheduling (M-I-T)
10. Dynamic Programming: Advanced DP (M-I-T)
11. Dynamic Programming: All-Pairs Shortest Paths (M-I-T)
12. Greedy Algorithms: Minimum Spanning Tree (M-I-T)
13. Incremental Improvement: Max Flow, Min Cut (M-I-T)
14. Incremental Improvement: Matching (M-I-T)
15. Linear Programming: LP, reductions, Simplex (M-I-T)
16. Complexity: P, NP, NP-completeness, Reductions (M-I-T)
R10. Distributed Algorithms (M-I-T)
R11. Cryptography: More Primitives (M-I-T)
17. Complexity: Approximation Algorithms (M-I-T)
R2. 2-3 Trees and B-Trees (M-I-T)
18. Complexity: Fixed-Parameter Algorithms (M-I-T)
19. Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees (M-I-T)
R4. Randomized Select and Randomized Quicksort (M-I-T)
2. Divide & Conquer: Convex Hull, Median Finding (M-I-T)
R5. Dynamic Programming (M-I-T)
20. Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees (M-I-T)
R6. Greedy Algorithms (M-I-T)
21. Cryptography: Hash Functions (M-I-T)

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