Lectures on Proof Verification and Approximation Algorithms

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Mayr, Ernst W. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Prömel, Hans Jürgen (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Steger, Angelika (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1998.
• Έκδοση: 1st ed. 1998.
• Σειρά: Lecture Notes in Computer Science, 1367
• Θέματα: Computers.; Algorithms.; Computer science-Mathematics.; Combinatorics.; Calculus of variations.; Theory of Computation.; Algorithm Analysis and Problem Complexity.; Discrete Mathematics in Computer Science.; Computation by Abstract Devices.; Calculus of Variations and Optimal Control; Optimization.

 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. 

 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.