Introduction to Mathematical Optimization

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.