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Viser: Numerical Optimization
Numerical Optimization
Jorge Nocedal og Stephen J. Wright
(2006)
Sprog: Engelsk
Springer New York
555,00 kr.
499,50 kr.
10 stk på lager
Hvor er varen på lager?om ca. 2 hverdage
Detaljer Om Varen
- Hardback: 664 sider
- Udgiver: Springer New York (Juli 2006)
- Forfattere: Jorge Nocedal og Stephen J. Wright
- ISBN: 9780387303031
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.
Preface
-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.
-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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