## ＜電子ブック＞Convex Optimization with Computational Errors

責任表示 by Alexander J. Zaslavski Zaslavski, Alexander J SpringerLink (Online service) 1st ed. 2020. English (英語) Springer International Publishing Imprint: Springer 2020- Cham, Germany シリーズ Springer Optimization and Its Applications ; 155 This book studies approximate solutions of optimization problems in the presence of computational errors. It contains a number of results on the convergence behavior of algorithms in a Hilbert space, ...which are well known as important tools for solving optimization problems. The research presented continues from the author's (c) 2016 book Numerical Optimization with Computational Errors. Both books study algorithms taking into account computational errors which are always present in practice. The main goal is, for a known computational error, to obtain the approximate solution and the number of iterations needed. The discussion takes into consideration that for every algorithm, its iteration consists of several steps; computational errors for various steps are generally different. This fact, which was not accounted for in the previous book, is indeed important in practice. For example, the subgradient projection algorithm consists of two steps-a calculation of a subgradient of the objective function and a calculation of a projection on the feasible set. In each of these two steps there is a computational error and these two computational errors are generally different. The book is of interest for researchers and engineers working in optimization. It also can be useful in preparation courses for graduate students. The main feature of the book will appeal specifically to researchers and engineers working in optimization as well as to experts in applications of optimization to engineering and economics.続きを見る Preface1. Introduction2. Subgradient Projection Algorithm3. The Mirror Descent Algorithm4. Gradient Algorithm with a Smooth Objective Function5. An Extension of the Gradient Algorithm6. Continuous Subgradient Method7. An optimization problems with a composite objective function8. A zero-sum game with two-players9. PDA-based method for convex optimization10 Minimization of quasiconvex functions.-11. Minimization of sharp weakly convex functions.-12. A Projected Subgradient Method for Nonsmooth ProblemsReferences. -Index. .続きを見る http://hdl.handle.net/2324/1001694130 Full text available from Springer Mathematics and Statistics eBooks 2020 English/International

### 詳細

レコードID 3204888 QA315-316 QA402.3 515.64 Calculus of variations. Computer mathematics. Calculus of Variations and Optimal Control; Optimization. Computational Mathematics and Numerical Analysis. ssj0002287047 9783030378219(print) 9783030378233(print) 9783030378240(print) 9783030378226 2020.06.27 2020.06.28