1. Course Overview, Interval Scheduling
1:23:342. Divide & Conquer: Convex Hull, Median Finding
1:20:34R1. Matrix Multiplication and the Master Theorem
53:463. Divide & Conquer: FFT
1:20:52R2. 2-3 Trees and B-Trees
30:454. Divide & Conquer: van Emde Boas Trees
1:20:145. Amortization: Amortized Analysis
1:15:536. Randomization: Matrix Multiply, Quicksort
1:21:52Lecture 7: Randomization: Skip Lists (6.046J)
1:20:56Lecture 8: Randomization: Universal & Perfect Hashing (6.046J)
1:21:51Lecture 9: Augmentation: Range Trees (6.046J)
1:24:34Lecture 10: Dynamic Programming: Advanced DP (6.046J)
1:20:08Lecture 11: Dynamic Programming: All-Pairs Shortest Paths (6.046J)
1:21:49Lecture 12: Greedy Algorithms: Minimum Spanning Tree (6.046J)
1:22:10Lecture 13: Incremental Improvement: Max Flow, Min Cut (6.046J)
1:22:58Lecture 14: Incremental Improvement: Matching (6.046J)
1:22:32Lecture 15: Linear Programming: LP, Reductions, Simplex (6.046J)
1:22:27Lecture 16: Complexity: P, NP, NP-completeness, Reductions (6.046J)
1:25:256. Randomization: Matrix Multiply, Quicksort
Professor of Electrical Engineering and Computer Science
This lecture introduces the principles of randomized algorithms, illustrating their application through the analysis of probabilistic approaches to matrix multiplication verification and the Quicksort algorithm.
MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Srinivas Devadas
5. Amortization: Amortized Analysis
Next · Lecture 9Lecture 7: Randomization: Skip Lists (6.046J)
Seminar notes & discussion
No public seminar notes on this lecture yet.