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MIT 6.046J: Design and Analysis of Algorithms →0/18
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1. Course Overview, Interval Scheduling

1:23:34
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2. Divide & Conquer: Convex Hull, Median Finding

1:20:34
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R1. Matrix Multiplication and the Master Theorem

53:46
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3. Divide & Conquer: FFT

1:20:52
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R2. 2-3 Trees and B-Trees

30:45
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4. Divide & Conquer: van Emde Boas Trees

1:20:14
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5. Amortization: Amortized Analysis

1:15:53
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6. Randomization: Matrix Multiply, Quicksort

1:21:52
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Lecture 7: Randomization: Skip Lists (6.046J)

1:20:56
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Lecture 8: Randomization: Universal & Perfect Hashing (6.046J)

1:21:51
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Lecture 9: Augmentation: Range Trees (6.046J)

1:24:34
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Lecture 10: Dynamic Programming: Advanced DP (6.046J)

1:20:08
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Lecture 11: Dynamic Programming: All-Pairs Shortest Paths (6.046J)

1:21:49
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Lecture 12: Greedy Algorithms: Minimum Spanning Tree (6.046J)

1:22:10
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Lecture 13: Incremental Improvement: Max Flow, Min Cut (6.046J)

1:22:58
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Lecture 14: Incremental Improvement: Matching (6.046J)

1:22:32
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Lecture 15: Linear Programming: LP, Reductions, Simplex (6.046J)

1:22:27
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Lecture 16: Complexity: P, NP, NP-completeness, Reductions (6.046J)

1:25:25

Lecture 16: Complexity: P, NP, NP-completeness, Reductions (6.046J)

Erik DemaineMIT

Professor

1:25:25·2016·undergraduate level

Professor Demaine systematically introduces the fundamental concepts of computational complexity theory, covering the definitions of P, NP, and NP-completeness. The lecture details the critical role of polynomial-time reductions in establishing the relationships between decision problems.

Theory & AlgosComputational ComplexityAlgorithms & Data StructuresP vs NPNP-Completeness

MIT 6.046J Design and Analysis of Algorithms, Spring 2015

View the complete course: http://ocw.mit.edu/6-046JS15

Instructor: Erik Demaine

Previous · Lecture 17

Lecture 15: Linear Programming: LP, Reductions, Simplex (6.046J)

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