Front Cover -- Fundamentals of Optimization Techniques With Algorithms -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- 1. Introduction to optimization -- 1.1 Optimal problem formulation -- 1.1.1 Design variables -- 1.1.2 Constraints -- 1.1.3 Objective function -- 1.1.4 Variable bounds -- 1.2 Engineering applications of optimization -- 1.3 Optimization techniques -- Further reading -- 2. Linear programming -- 2.1 Formulation of the problem -- Practice set 2.1 -- 2.2 Graphical method -- 2.2.1 Working procedure -- Practice set 2.2 -- 2.3 General LPP
2.3.1 Canonical and standard forms of LPP -- Practice set 2.3 -- 2.4 Simplex method -- 2.4.1 Reduction of feasible solution to a basic feasible solution -- 2.4.2 Working procedure of the simplex method -- Practice set 2.4 -- 2.5 Artificial variable techniques -- 2.5.1 Big M method -- 2.5.2 Two-phase method -- Practice set 2.5 -- 2.6 Duality Principle -- 2.6.1 Formulation of a dual problem -- 2.6.1.1 Formulation of a dual problem when the primal has equality constraints -- 2.6.1.2 Duality principle -- Practice set 2.6 -- 2.7 Dual simplex method -- 2.7.1 Working procedure for a dual simplex method
Practice set 2.7 -- Further reading -- 3. Single-variable nonlinear optimization -- 3.1 Classical method for single-variable optimization -- 3.2 Exhaustive search method -- 3.3 Bounding phase method -- 3.4 Interval halving method -- 3.5 Fibonacci search method -- 3.6 Golden section search method -- 3.7 Bisection method -- 3.8 Newton-Raphson method -- 3.9 Secant method -- 3.10 Successive quadratic point estimation method -- Further reading -- 4. Multivariable unconstrained nonlinear optimization -- 4.1 Classical method for multivariable optimization
