Introduction to Mathematical Optimization: From Linear Programming to Metaheuristics

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Cambridge International Science Publishing, Jan 1, 2008 - Mathematics - 150 pages
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This book strives to provide a balanced coverage of efficient algorithms commonly used in solving mathematical optimization problems. It covers both the convectional algorithms and modern heuristic and metaheuristic methods. Topics include gradient-based algorithms such as Newton-Raphson method, steepest descent method, Hooke-Jeeves pattern search, Lagrange multipliers, linear programming, particle swarm optimization (PSO), simulated annealing (SA), and Tabu search. Multiobjective optimization including important concepts such as Pareto optimality and utility method is also described. Three Matlab and Octave programs so as to demonstrate how PSO and SA work are provided. An example of demonstrating how to modify these programs to solve multiobjective optimization problems using recursive method is discussed.

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Norms and Hessian Matrices
RootFinding Algorithms
System of Linear Equations

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About the author (2008)

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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