Contents -- Preface -- 1. Mathematical Optimization -- 1.1 Optimization -- 1.2 Optimality Criteria -- 1.3 Computational Complexity -- 1.4 NP-Complete Problems -- 2. Norms and Hessian Matrices -- 2.1 Vector and Matrix Norms -- 2.2 Eigenvalues and Eigenvectors -- 2.3 Spectral Radius of Matrices -- 2.4 Hessian Matrix -- 2.5 Convexity -- 3. Root-Finding Algorithms -- 3.1 Simple Iterations -- 3.2 Bisection Method -- 3.3 Newton's Method -- 3.4 Iteration Methods -- 4. System of Linear Equations -- 4.1 Linear systems -- 4.2 Gauss Elimination -- 4.3 Gauss-Jordan Elimination -- 4.4 LU Factorization -- 4.5 Iteration Methods -- 4.5.1 Jacobi Iteration Method -- 4.5.2 Gauss-Seidel Iteration -- 4.5.3 Relaxation Method -- 4.6 Nonlinear Equation -- 4.6.1 Simple Iterations -- 4.6.2 Newton-Raphson Method -- 5. Unconstrained Optimization -- 5.1 Univariate Functions -- 5.2 Multivariate Functions -- 5.3 Gradient-Based Methods -- 5.3.1 Newton's Method -- 5.3.2 Steepest Descent Method -- 5.4 Hooke-Jeeves Pattern Search -- 6.Linear Mathematical Programming -- 6.1 Linear Programming -- 6.2 Simplex Method -- 6.2.1 Basic Procedure -- 6.2.2 Augmented Form -- 6.2.3 A Case Study -- 7. Nonlinear Optimization -- 7.1 Penalty Method -- 7.2 Lagrange Multipliers -- 7.3 Kuhn-Tucker Conditions -- 7.4 No Free Lunch Theorems -- 8. Tabu Search -- 8.1 Tabu Search -- 8.2 Travelling Salesman Problem -- 8.3 Tabu Search for TSP -- 9. Ant Colony Optimization -- 9.1 Behaviour of Ants -- 9.2 Ant Colony Optimization -- 9.3 Double Bridge Problem -- 9.4 Multi-Peak Functions -- 10. Particle Swarm Optimization -- 10.1 Swarm Intelligence -- 10.2 PSO algorithms -- 10.3 Accelerated PSO -- 10.4 Multimodal Functions -- 10.5 Implementation -- 10.6 Constraints -- 11. Simulated Annealing -- 11.1 Fundamental Concepts -- 11.2 Choice of Parameters -- 11.3 SA Algorithm -- 11.4 Implementation.

12. Multiobjective Optimization -- 12.1 Pareto Optimality -- 12.2 Weighted Sum Method -- 12.3 Utility Method -- 12.4 Metaheuristic Search -- 12.5 Other Algorithms -- Bibliography -- Index.