Inhaltsangabe
This book provides a balanced selection of both conventional optimization algorithms and modern metaheuristic methods commonly used in mathematical optimization. Conventional algorithms include gradient-based methods such as the steepest descent method, the simplex method for linear programming, Lagrange multipliers, and Hooke-Jeeves pattern search. Metaheuristic methods include ant colony optimization (ACO), particle swarm optimization (PSO), simulated annealing (SA), and Tabu search, recursive method for multiobjective optimization. With dozens of worked examples and three Matlab/Octave programs, this book can ideally serve as a textbook, especially suitable for undergraduates and graduates.
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Reseña del editor
This book provides a balanced selection of both conventional optimization algorithms and modern metaheuristic methods commonly used in mathematical optimization. Conventional algorithms include gradient-based methods such as the steepest descent method, the simplex method for linear programming, Lagrange multipliers, and Hooke-Jeeves pattern search. Metaheuristic methods include ant colony optimization (ACO), particle swarm optimization (PSO), simulated annealing (SA), and Tabu search, recursive method for multiobjective optimization. With dozens of worked examples and three Matlab/Octave programs, this book can ideally serve as a textbook, especially suitable for undergraduates and graduates.
Biografía del autor
Xin-She Yang received his DPhil in applied mathematics from the University of Oxford. He is currently a research fellow at the Univer-sity of Cambridge. He is also the author of the book ""An Introduction to Computational Engineering With Matlab (CISP, 2006).
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Introduction to Mathematical Optimization: From Linear Programming to Metaheuristics
Verlag:
Cambridge Intl Science Pub, 2008
ISBN 10: 1904602827
ISBN 13: 9781904602828
Gebraucht
Softcover
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