Numerical Optimization / Edition 2by Jorge Nocedal, Stephen Wright
Pub. Date: 09/15/2009
Publisher: Springer New York
Optimization is an important tool used in decision science and for the analysis of physical systems used in engineering. One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.
- Springer New York
- Publication date:
- Springer Series in Operations Research and Financial Engineering
- Edition description:
- 2nd ed. 2006
- Sales rank:
- Product dimensions:
- 7.01(w) x 9.25(h) x 0.06(d)
Table of ContentsPreface.-Preface to the Second Edition.-Introduction.-Fundamentals of Unconstrained Optimization.-Line Search Methods.-Trust-Region Methods.-Conjugate Gradient Methods.-Quasi-Newton Methods.-Large-Scale Unconstrained Optimization.-Calculating Derivatives.-Derivative-Free Optimization.-Least-Squares Problems.-Nonlinear Equations.-Theory of Constrained Optimization.-Linear Programming: The Simplex Method.-Linear Programming: Interior-Point Methods.-Fundamentals of Algorithms for Nonlinear Constrained Optimization.-Quadratic Programming.-Penalty and Augmented Lagrangian Methods.-Sequential Quadratic Programming.-Interior-Point Methods for Nonlinear Programming.-Background Material.- Regularization Procedure.
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This book presents very clear pictures of fundamental theory and algorithms in numerical optimization. I especially like the way that it universally put Newton, Quazi-Newton, Conjugate Gradient methods into single framework and explains them in a very intuitive-friend way. To those who want to learn numerical optimization, I can only say: Read the Book.