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)
1:25:252. Divide & Conquer: Convex Hull, Median Finding
This lecture introduces the divide-and-conquer paradigm, applying it to fundamental computational geometry problems including the determination of a convex hull and efficient median finding algorithms.
MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Srinivas Devadas
1. Course Overview, Interval Scheduling
Next · Lecture 3R1. Matrix Multiplication and the Master Theorem