Local Search in Combinatorial Optimization
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Abstraction (software engineering)
Algorithm
Algorithm design
Analysis of algorithms
Approximation algorithm
Artificial neural network
Autocorrelation
Bayesian inference
Best
Binary search algorithm
Cache algorithms
Category=PBU
Category=PBV
Cluster analysis
Combinatorial optimization
Computation
Computational complexity theory
Computational model
Computational problem
Computational resource
Continuous optimization
Decision problem
Decision theory
Deterministic algorithm
Dynamic programming
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Evolutionary computation
Expander graph
Function approximation
Genetic algorithm
Greedy algorithm
Heuristic
Hirsch conjecture
Hybrid algorithm
Implementation
Instance (computer science)
Iteration
Kruskal's algorithm
Linear complementarity problem
Linear programming
Linear programming relaxation
Local optimum
Local search (optimization)
Log-space reduction
Loss function
Matching (graph theory)
Mathematical optimization
Metropolis-Hastings algorithm
Model of computation
NP-hardness
Optimality criterion
Optimization problem
P versus NP problem
Parameter (computer programming)
Partition problem
Polynomial-time approximation scheme
Polynomial-time reduction
Probabilistic method
Processing (programming language)
Quadratic assignment problem
Randomized algorithm
Ranking (information retrieval)
Result
Scheduling (computing)
Search algorithm
Search problem
Search tree
Self-organizing map
Simplex algorithm
Tabu search
Time complexity
Travelling salesman problem
Variable (computer science)
worst and average case
Product details
- ISBN 9780691115221
- Weight: 709g
- Dimensions: 152 x 235mm
- Publication Date: 03 Aug 2003
- Publisher: Princeton University Press
- Publication City/Country: US
- Product Form: Paperback
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In the past three decades, local search has grown from a simple heuristic idea into a mature field of research in combinatorial optimization that is attracting ever-increasing attention. Local search is still the method of choice for NP-hard problems as it provides a robust approach for obtaining high-quality solutions to problems of a realistic size in reasonable time. Local Search in Combinatorial Optimization covers local search and its variants from both a theoretical and practical point of view, each topic discussed by a leading authority. This book is an important reference and invaluable source of inspiration for students and researchers in discrete mathematics, computer science, operations research, industrial engineering, and management science. In addition to the editors, the contributors are Mihalis Yannakakis, Craig A. Tovey, Jan H. M. Korst, Peter J. M. van Laarhoven, Alain Hertz, Eric Taillard, Dominique de Werra, Heinz Muhlenbein, Carsten Peterson, Bo Soderberg, David S. Johnson, Lyle A. McGeoch, Michel Gendreau, Gilbert Laporte, Jean-Yves Potvin, Gerard A. P. Kindervater, Martin W. P. Savelsbergh, Edward J. Anderson, Celia A. Glass, Chris N. Potts, C. L.
Liu, Peichen Pan, Iiro Honkala, and Patric R. J. Ostergard.
Emile Aarts is Vice-President and Scientific Program Director of the Philips Research Laboratories, Eindhoven, and a Professor of Computer Science at Eindhoven University of Technology. Jan Karel Lenstra is the John P. Hunter Chair and Professor of Industrial and Systems Engineering at the Georgia Institute of Technology.
