UID:
almahu_9948621436202882
Umfang:
XII, 348 p.
,
online resource.
Ausgabe:
1st ed. 1998.
ISBN:
9783540697015
Serie:
Lecture Notes in Computer Science, 1367
Inhalt:
During the last few years, we have seen quite spectacular progress in the area of approximation algorithms: for several fundamental optimization problems we now actually know matching upper and lower bounds for their approximability. This textbook-like tutorial is a coherent and essentially self-contained presentation of the enormous recent progress facilitated by the interplay between the theory of probabilistically checkable proofs and aproximation algorithms. The basic concepts, methods, and results are presented in a unified way to provide a smooth introduction for newcomers. These lectures are particularly useful for advanced courses or reading groups on the topic.
Anmerkung:
to the theory of complexity and approximation algorithms -- to randomized algorithms -- Derandomization -- Proof checking and non-approximability -- Proving the PCP-Theorem -- Parallel repetition of MIP(2,1) systems -- Bounds for approximating MaxLinEq3-2 and MaxEkSat -- Deriving non-approximability results by reductions -- Optimal non-approximability of MaxClique -- The hardness of approximating set cover -- Semidefinite programming and its applications to approximation algorithms -- Dense instances of hard optimization problems -- Polynomial time approximation schemes for geometric optimization problems in euclidean metric spaces.
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783540642015
Weitere Ausg.:
Printed edition: ISBN 9783662171806
Sprache:
Englisch
URL:
https://doi.org/10.1007/BFb0053010
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