Approximate Local Search in Combinatorial Optimization
Abstract
Local search algorithms for combinatorial optimization problems are in general of pseudopolynomial running time and polynomial-time algorithms are often not known for finding locally optimal solutions for NP-hard optimization problems. We introduce the concept of epsilon-local optimality and show that an epsilon-local optimum can be identified in time polynomial in the problem size and 1/epsilon whenever the corresponding neighborhood can be searched in polynomial time, for epsilon > 0. If the neighborhood can be searched in polynomial time for a delta-local optimum, we present an algorithm that produces a (delta+epsilon)-local optimum in time polynomial in the problem size and 1/epsilon. As a consequence, a combinatorial optimization problem has a fully polynomial-time approximation scheme if and only if it has a fully polynomial-time augmentation schemDownload Info
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Paper provided by Massachusetts Institute of Technology (MIT), Sloan School of Management in its series Working papers with number 4325-03.
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Date of creation: 15 Aug 2003
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Handle: RePEc:mit:sloanp:3539
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Postal: MASSACHUSETTS INSTITUTE OF TECHNOLOGY (MIT), SLOAN SCHOOL OF MANAGEMENT, 50 MEMORIAL DRIVE CAMBRIDGE MASSACHUSETTS 02142 USA
For corrections or technical questions regarding this item, or to correct its listing, contact: (Christian Zimmermann).
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Keywords: Local Search; Neighborhood Search; Approximation Algorithms; Computational Complexity; Combinatorial Optimization; 0/1-Integer Programming;References
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