Optimization: Theory and PracticeOptimization: Theory and Practice is ideally suited for a first course on optimization. It gives a detailed mathematical exposition to various optimization techniques. The presentation style retains abstract flavor of the mathematical framework as well as applicability potential of techniques, thereby making the text useful to both scientists and engineers. |
Contents
Mathematical Preliminaries | 1 |
Tangent line to a convex function | 41 |
Two point Equal Interval Search Convergence of iterates | 58 |
for minimization of fx x 1x 2x 3 in 13 | 79 |
of fx x 1x 2x 3 in 13 | 88 |
Unconstrained Gradient Based Optimization Methods | 95 |
References | 172 |
Constrained Optimization Methods | 238 |
299 | |
parameters 5 parameter model and experimental profile | 316 |
References | 320 |
Common terms and phrases
Ağ(k basic feasible solution basic variable becomes nonbasic BFGS method coefficient Comparison of convergence compute convergence of iterates convex convex function corresponding critical point defined denoted DFP method Eason and Fenton equation evaluations Example extreme point feasible set Fenton function Fibonacci Figure Fletcher-Reeves following theorem function by BFGS function f(x function values given by Eq gives global minimizer Golden Section Search gradient hence implies inequality Initial guess Interpolation Algorithm iterates for minimization Kuhn-Tucker conditions Let f linear programming Marquardt's method matrix maximizer minimization of f(x minimization problem minimum Newton's method nonbasic variable objective function optimal solution optimization problem orthogonal parameters penalty function positive definite problem given Proof quadratic function queue Rosenbrock function Secant Method simplex algorithm simplex method Solve steepest descent steepest descent method Table unconstrained minimization Vƒ(x απ