30 episodes
Algorithm Design and Analysis Dan Gusfield
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The purpose of this undergraduate course is to introduce fundamental techniques and viewpoints for the design and the analysis of efficient computer algorithms, and to study important specific algorithms. The course relies heavily on mathematics and mathematical thinking in two ways: first as a way of proving properties about particular algorithms such as termination, and correctness; and second, as a way of establishing bounds on the worst case (or average case) use of some resource, usually time, by a specific algorithm. The course covers some randomized algorithms as well as deterministic algorithms.
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Coping with NP-completeness
Lecture 28: Gusfield recaps NP-completeness.
The professor discusses coping with NP-complete problems: approximation algorithms and lowering the exponent of exponential-time algorithms. -
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Major theorems of NP-completeness
Lecture 27 covers the major theorems of NP-completeness, P = NP question, and how to prove a new problem in NP-complete.
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Formal definition of P and NP
In Lecture 26, Gusfield gives correct, formal definitions of P and NP, ending with a brief definition of NP-complete problems (languages).
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An intuitive view of NP
Lecture 25 deals with an intuitive view of NP - not the correct formal definition.
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Introduction to P and NP
Lecture 24 gives an introduction to P and NP and polynomial-time reductions.
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Introduction to approximation algorithms
Lecture 23 covers approximation algorithms - definition, factor of two approximation for the center cover problem.
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