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＜電子ブック＞
Introduction to Continuous Optimization / by Roman A. Polyak
(Springer Optimization and Its Applications. ISSN:19316836 ; 172)

版	1st ed. 2021.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2021
本文言語	英語
大きさ	XVI, 541 p. 25 illus., 1 illus. in color : online resource
著者標目	*Polyak, Roman A author SpringerLink (Online service)
件　名	LCSH:Mathematical optimization LCSH:Computer science -- Mathematics  全ての件名で検索 LCSH:Mathematical models FREE:Continuous Optimization FREE:Mathematical Applications in Computer Science FREE:Mathematical Modeling and Industrial Mathematics
一般注記	1. Introduction -- 2. Elements of Calculus and Convex Analysis -- 3. Few Topics in Unconstrained Optimization -- 4. Optimization with Equality Constraints -- 5. Basics in Linear and Convex Optimization -- Self-Concordant Functions and IPM Complexity -- 7. Nonlinear Rescaling. Theory and Methods -- 8. Realizations of the NR Principle -- 9. Lagrangian Transformation and Interior Ellipsoid Methods -- 10. Finding Nonlinear Equilibrium -- 11. (With Igor Griva) Applications and Numerical Results -- Concluding Remarks -- Appendix -- References This self-contained monograph presents the reader with an authoritative view of Continuous Optimization, an area of mathematical optimization that has experienced major developments during the past 40 years. The book contains results which have not yet been covered in a systematic way as well as a summary of results on NR theory and methods developed over the last several decades. The readership is aimed to graduate students in applied mathematics, computer science, economics, as well as researchers working in optimization and those applying optimization methods for solving real life problems. Sufficient exercises throughout provide graduate students and instructors with practical utility in a two-semester course in Continuous Optimization. The topical coverage includes interior point methods, self-concordance theory and related complexity issues, first and second order methods with accelerated convergence, nonlinear rescaling (NR) theory and exterior point methods, just to mention a few. The book contains a unified approach to both interior and exterior point methods with emphasis of the crucial duality role. One of the main achievements of the book shows what makes the exterior point methods numerically attractive and why. The book is composed in five parts. The first part contains the basics of calculus, convex analysis, elements of unconstrained optimization, as well as classical results of linear and convex optimization. The second part contains the basics of self-concordance theory and interior point methods, including complexity results for LP, QP, and QP with quadratic constraint, semidefinite and conic programming. In the third part, the NR and Lagrangian transformation theories are considered and exterior point methods are described. Three important problems in finding equilibrium are considered in the fourth part. In the fifth and final part of the book, several important applications arising in economics, structural optimization, medicine, statistical learning theory, and more, are detailed. Numerical results, obtained by solving a number of real life and test problems, are also provided HTTP:URL=https://doi.org/10.1007/978-3-030-68713-7

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