Introduction to Mathematical Optimization - From Linear Programming to Metaheuristics

  • ID: 1551634
  • Report
  • 160 Pages
  • CISP - Cambridge International Science Publishing
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This book provides a balanced selection of both conventional optimization algorithms and modern metaheuristic methods commonly used in mathematical optimization. Conventionalalgorithms 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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I. Fundamentals;
1 Mathematical Optimization;
2 Norms and Hessian Matrices;
3 Root-Finding Algorithms;
4 System of Linear Equations;

II Mathematical Optimization;
5 Unconstrained Optimization;
6 Linear Mathematical Programming;
7 Nonlinear Optimization;

III Metaheuristic Methods;
8 Tabu Search;
9 Ant Colony Optimization;
10 Particle Swarm Optimization;
11 Simulated Annealing;
12 Multiobjective Optimization;
Bibliography;
Index
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