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＜電子ブック＞
Nonlinear Optimization with Engineering Applications / by Michael Bartholomew-Biggs
(Springer Optimization and Its Applications. ISSN:19316836 ; 19)

版	1st ed. 2008.
出版者	(New York, NY : Springer US : Imprint: Springer)
出版年	2008
本文言語	英語
大きさ	XVI, 280 p : online resource
著者標目	*Bartholomew-Biggs, Michael author SpringerLink (Online service)
件　名	LCSH:Mathematical optimization LCSH:Calculus of variations LCSH:Operations research LCSH:Management science FREE:Optimization FREE:Calculus of Variations and Optimization FREE:Operations Research, Management Science
一般注記	Introducing Optimization -- One-variable Optimization -- Applications in n Variables -- n-Variable Unconstrained Optimization -- Direct Search Methods -- Computing Derivatives -- The Steepest Descent Method -- Weak Line Searches and Convergence -- Newton and Newton-like Methods -- Quasi-Newton Methods -- Conjugate Gradient Methods -- ASummary of Unconstrained Methods -- Optimization with Restrictions -- Larger-Scale Problems -- Global Unconstrained Optimization -- Equality Constrained Optimization -- Linear Equality Constraints -- Penalty Function Methods -- Sequential Quadratic Programming -- Inequality Constrained Optimization -- Extending Equality Constraint Methods -- Barrier Function Methods -- Interior Point Methods -- A Summary of Constrained Methods -- The OPTIMA Software This textbook examines a broad range of problems in science and engineering, describing key numerical methods applied to real life. The case studies presented are in such areas as data fitting, vehicle route planning and optimal control, scheduling and resource allocation, sensitivity calculations and worst-case analysis. Among the main topics covered: * one-variable optimization — optimality conditions, direct search and gradient * unconstrained optimization in n variables — solution methods including Nelder and Mead simplex, steepest descent, Newton, Gauss–Newton, and quasi-Newton techniques, trust regions and conjugate gradients * constrained optimization in n variables — solution methods including reduced-gradients, penalty and barrier methods, sequential quadratic programming, and interior point techniques * an introduction to global optimization * an introduction to automatic differentiation Chapters are self-contained with exercises provided at the end of most sections. Nonlinear Optimization with Engineering Applications is ideal for self-study and classroom use in engineering courses at the senior undergraduate or graduate level. The book will also appeal to postdocs and advanced researchers interested in the development and use of optimization algorithms. Also by the author: Nonlinear Optimization with Financial Applications, ISBN: 978-1-4020-8110-1, (c)2005, Springer HTTP:URL=https://doi.org/10.1007/978-0-387-78723-7

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