The Projected Subgradient Algorithm in Convex Optimization. 1st ed. 2020

種類:

電子ブック

責任表示:

by Alexander J. Zaslavski

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2020

著者名:

• Zaslavski, Alexander J.
• SpringerLink (Online service)

シリーズ名:

SpringerBriefs in Optimization ;

ISBN:

9783030603007 [3030603008]    

注記:

1. Introduction -- 2. Nonsmooth Convex Optimization -- 3. Extensions -- 4. Zero-sum Games with Two Players -- 5. Quasiconvex Optimization -- References.
This focused monograph presents a study of subgradient algorithms for constrained minimization problems in a Hilbert space. The book is of interest for experts in applications of optimization to engineering and economics. The goal is to obtain a good approximate solution of the problem in the presence of computational errors. The discussion takes into consideration the fact that for every algorithm its iteration consists of several steps and that computational errors for different steps are different, in general. The book is especially useful for the reader because it contains solutions to a number of difficult and interesting problems in the numerical optimization. The subgradient projection algorithm is one of the most important tools in optimization theory and its applications. An optimization problem is described by an objective function and a set of feasible points. For this algorithm each iteration consists of two steps. The first step requires a calculation of a subgradient of the objective function; the

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