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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.
更新頻率:
daily
平均音訊長度:
46 minutes
英語
美國
60 集
自從 2010年9月23日
episodic
最新一集 🔗
Lecture 28: Gusfield recaps NP-completeness.
The professor discusses coping with NP-complete problems: approximation algorithms and lowering the exponent of exponential-time algorithms.
以前的節目 🔗
Lecture 28: Gusfield recaps NP-completeness.
The professor discusses coping with NP-complete problems: approximation algorithms and lowering the exponent of exponential-time algorithms.
Lecture 27 covers the major theorems of NP-completeness, P = NP question, and how to prove a new problem in NP-complete.
Lecture 27 covers the major theorems of NP-completeness, P = NP question, and how to prove a new problem in NP-complete.
In Lecture 26, Gusfield gives correct, formal definitions of P and NP, ending with a brief definition of NP-complete problems (languages).
In Lecture 26, Gusfield gives correct, formal definitions of P and NP, ending with a brief definition of NP-complete problems (languages).
Lecture 25 deals with an intuitive view of NP - not the correct formal definition.
Lecture 25 deals with an intuitive view of NP - not the correct formal definition.
Lecture 24 gives an introduction to P and NP and polynomial-time reductions.
Lecture 24 gives an introduction to P and NP and polynomial-time reductions.
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