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Lectures on Proof Verification and Approximation Algorithms [electronic resource] /

Contributor(s): Mayr, Ernst W [editor.] | Jürgen Prömel, Hans [editor.] | Steger, Angelika [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 1367Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1998.Description: XII, 348 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540697015.Subject(s): Computer science | Computers | Algorithms | Computer science -- Mathematics | Calculus of variations | Combinatorics | Computer Science | Theory of Computation | Algorithm Analysis and Problem Complexity | Discrete Mathematics in Computer Science | Computation by Abstract Devices | Combinatorics | Calculus of Variations and Optimal Control; OptimizationOnline resources: Click here to access online
Contents:
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 eBooksSummary: 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.
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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.

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.

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