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Algorithm Design and Analysis
By Dan Gusfield
Algorithm Design and Analysis episodes
- Coping with NP-completenessLecture 28: Gusfield recaps NP-completeness.The professor discusses coping with NP-complete problems: approximation algorithms and lowering the exponent of exponential-time algorithms.40 min
- Major theorems of NP-completenessLecture 27 covers the major theorems of NP-completeness, P = NP question, and how to prove a new problem in NP-complete.51 min
- Formal definition of P and NPIn Lecture 26, Gusfield gives correct, formal definitions of P and NP, ending with a brief definition of NP-complete problems (languages).46 min
- An intuitive view of NPLecture 25 deals with an intuitive view of NP - not the correct formal definition.49 min
- Introduction to P and NPLecture 24 gives an introduction to P and NP and polynomial-time reductions.51 min
- Introduction to approximation algorithmsLecture 23 covers approximation algorithms - definition, factor of two approximation for the center cover problem.48 min
- Finish of linear-time pattern matchingLecture 22 completes linear-time pattern matching using the Z-algorithm52 min
- Linear-time pattern matching. Z-values and Z-algorithmIn Lecture 21, Gusfield covers linear-time pattern matching. He also discusses Z-values and Z-algorithms.52 min
- Unique-Decipherability. Graph algorithm and proof of correctnessLecture 20 deals with unique-decipherability: efficient graph-based algorithm and proof of correctness.52 min
- The unique-decipherability problemLecture 19 covers the unique-decipherability problem and gives definitions and the start of a graph-based solution.53 min
About Algorithm Design and Analysis
From the publisher's feed
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.