The Projected Subgradient Algorithm in Convex Optimization

Author:   Alexander J. Zaslavski
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
ISBN:  

9783030602994


Pages:   146
Publication Date:   26 November 2020
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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The Projected Subgradient Algorithm in Convex Optimization


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Overview

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 second requires a calculation of a projection on the feasible set. The computational errors in each of these two steps are different.  This book shows that the algorithm discussed, generates a good approximate solution, if all the computational errors are bounded from above by a small positive constant. Moreover, if computational errors for the two steps of the algorithm are known, one discovers an approximate solution and how many iterations one needs for this.  In addition to their mathematical interest, the generalizations considered in this book have a significant practical meaning.

Full Product Details

Author:   Alexander J. Zaslavski
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Weight:   0.454kg
ISBN:  

9783030602994


ISBN 10:   3030602990
Pages:   146
Publication Date:   26 November 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

1. Introduction.- 2. Nonsmooth Convex Optimization.- 3. Extensions.-  4. Zero-sum Games with Two Players.- 5. Quasiconvex Optimization.- References.

Reviews

The book is rigorously written, and organized taking into account the cursiveness of reading. The long proofs of the theorems are placed in annexes to chapters, in order to emphasize the importance of every result in a generating methodology of studying and solving problems. (Gabriela Cristescu, zbMATH 1464.90063, 2021)


“The book is rigorously written, and organized taking into account the cursiveness of reading. The long proofs of the theorems are placed in annexes to chapters, in order to emphasize the importance of every result in a generating methodology of studying and solving problems.” (Gabriela Cristescu, zbMATH 1464.90063, 2021)


Author Information

​Alexander J. Zaslavski is professor in the Department of Mathematics, Technion-Israel Institute of Technology, Haifa, Israel.​

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