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The Projected Subgradient Algorithm in Convex Optimization, Zaslavski Alexander J.


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Автор: Zaslavski Alexander J.
Название:  The Projected Subgradient Algorithm in Convex Optimization
ISBN: 9783030602994
Издательство: Springer
Классификация:

ISBN-10: 3030602990
Обложка/Формат: Paperback
Страницы: 146
Вес: 0.23 кг.
Дата издания: 04.01.2021
Язык: English
Размер: 23.39 x 15.60 x 0.84 cm
Ссылка на Издательство: Link
Поставляется из: Германии
Описание: 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.


Convex Optimization

Автор: Stephen Boyd
Название: Convex Optimization
ISBN: 0521833787 ISBN-13(EAN): 9780521833783
Издательство: Cambridge Academ
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Цена: 17950.00 р.
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Описание: The focus of this book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.

A Branch-and-Bound Algorithm for Multiobjective Mixed-integer Convex Optimization

Автор: Stefan Rockt?schel
Название: A Branch-and-Bound Algorithm for Multiobjective Mixed-integer Convex Optimization
ISBN: 3658291486 ISBN-13(EAN): 9783658291488
Издательство: Springer
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Цена: 6986.00 р.
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Описание: Stefan Rocktaschel introduces a branch-and-bound algorithm that determines a cover of the efficient set of multiobjective mixed-integer convex optimization problems. He examines particular steps of this algorithm in detail and enhances the basic algorithm with additional modifications that ensure a more precise cover of the efficient set.

Convex and Set-Valued Analysis

Автор: Aram Arutyunov, Valeri Obukhovskii
Название: Convex and Set-Valued Analysis
ISBN: 3110460289 ISBN-13(EAN): 9783110460285
Издательство: Walter de Gruyter
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Цена: 11148.00 р.
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Описание: This textbook is devoted to a compressed and self-contained exposition of two important parts of contemporary mathematics: convex and set-valued analysis. In the first part, properties of convex sets, the theory of separation, convex functions and their differentiability, properties of convex cones in finite- and infinite-dimensional spaces are discussed. The second part covers some important parts of set-valued analysis. There the properties of the Hausdorff metric and various continuity concepts of set-valued maps are considered. The great attention is paid also to measurable set-valued functions, continuous, Lipschitz and some special types of selections, fixed point and coincidence theorems, covering set-valued maps, topological degree theory and differential inclusions. Contents: PrefacePart I: Convex analysisConvex sets and their propertiesThe convex hull of a set. The interior of convex setsThe affine hull of sets. The relative interior of convex setsSeparation theorems for convex setsConvex functionsClosedness, boundedness, continuity, and Lipschitz property of convex functionsConjugate functionsSupport functionsDifferentiability of convex functions and the subdifferentialConvex conesA little more about convex cones in infinite-dimensional spacesA problem of linear programmingMore about convex sets and convex hullsPart II: Set-valued analysisIntroduction to the theory of topological and metric spacesThe Hausdorff metric and the distance between setsSome fine properties of the Hausdorff metricSet-valued maps. Upper semicontinuous and lower semicontinuous set-valued mapsA base of topology of the spaceHc(X)Measurable set-valued maps. Measurable selections and measurable choice theoremsThe superposition set-valued operatorThe Michael theorem and continuous selections. Lipschitz selections. Single-valued approximationsSpecial selections of set-valued mapsDifferential inclusionsFixed points and coincidences of maps in metric spacesStability of coincidence points and properties of covering mapsTopological degree and fixed points of set-valued maps in Banach spacesExistence results for differential inclusions via the fixed point methodNotationBibliographyIndex

Convex analysis and nonlinear optimization

Автор: Borwein, Jonathan M. Lewis, Adrian S. (university Of Waterloo)
Название: Convex analysis and nonlinear optimization
ISBN: 1441921273 ISBN-13(EAN): 9781441921277
Издательство: Springer
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Цена: 8378.00 р.
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Описание: Optimization is a rich and thriving mathematical discipline, and the underlying theory of current computational optimization techniques grows ever more sophisticated. This new edition adds material on semismooth optimization, as well as several new proofs.

Multi-Period Trading Via Convex Optimization

Автор: Boyd Stephen, Busseti Enzo, Diamond Steven
Название: Multi-Period Trading Via Convex Optimization
ISBN: 1680833286 ISBN-13(EAN): 9781680833287
Издательство: Неизвестно
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Цена: 8966.00 р.
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Описание: Multi-Period Trading via Convex Optimization collects in one place the basic definitions, a careful description of the model, and discussion of how convex optimization can be used in multi-period trading, all in a common notation and framework.

Global Optimization with Non-Convex Constraints

Автор: Roman G. Strongin; Yaroslav D. Sergeyev
Название: Global Optimization with Non-Convex Constraints
ISBN: 0792364902 ISBN-13(EAN): 9780792364900
Издательство: Springer
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Цена: 43184.00 р.
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Описание: Presents a new approach to global non-convex constrained optimization. Problem dimensionality is reduced via space-filling curves and to economize the search, constraint is accounted separately (penalties are not employed). The multicriteria case is also considered.

Vector Optimization and Monotone Operators via Convex Duality

Автор: Sorin-Mihai Grad
Название: Vector Optimization and Monotone Operators via Convex Duality
ISBN: 3319361902 ISBN-13(EAN): 9783319361901
Издательство: Springer
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Цена: 13974.00 р.
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Описание: This book investigates several duality approaches for vector optimization problems, while also comparing them. Special attention is paid to duality for linear vector optimization problems, for which a vector dual that avoids the shortcomings of the classical ones is proposed.

Convex Optimization in Normed Spaces

Автор: Juan Peypouquet
Название: Convex Optimization in Normed Spaces
ISBN: 3319137093 ISBN-13(EAN): 9783319137094
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This work is intended to serve as a guide for graduate students and researchers who wish to get acquainted with the main theoretical and practical tools for the numerical minimization of convex functions on Hilbert spaces. Therefore, it contains the main tools that are necessary to conduct independent research on the topic.

Convex Analysis for Optimization: A Unified Approach

Автор: Brinkhuis Jan
Название: Convex Analysis for Optimization: A Unified Approach
ISBN: 3030418030 ISBN-13(EAN): 9783030418038
Издательство: Springer
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Цена: 11878.00 р.
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Описание: It starts with the concept of convex sets, their primal description, constructions, topological properties and dual description, and then moves on to convex functions and the fundamental principles of convex optimization and their use in the complete analysis of convex optimization problems by means of a systematic four-step method.

Non-Convex Multi-Objective Optimization

Автор: Panos M. Pardalos; Antanas ?ilinskas; Julius ?ilin
Название: Non-Convex Multi-Objective Optimization
ISBN: 3319610058 ISBN-13(EAN): 9783319610054
Издательство: Springer
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Цена: 12577.00 р.
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Описание: Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions.

Lagrange-type Functions in Constrained Non-Convex Optimization

Автор: Alexander M. Rubinov; Xiao-qi Yang
Название: Lagrange-type Functions in Constrained Non-Convex Optimization
ISBN: 1461348218 ISBN-13(EAN): 9781461348214
Издательство: Springer
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Цена: 13974.00 р.
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Описание: Thus the question arises how to generalize classical Lagrange and penalty functions, in order to obtain an appropriate scheme for reducing constrained optimiza- tion problems to unconstrained ones that will be suitable for sufficiently broad classes of optimization problems from both the theoretical and computational viewpoints.

Global Optimization with Non-Convex Constraints

Автор: Roman G. Strongin; Yaroslav D. Sergeyev
Название: Global Optimization with Non-Convex Constraints
ISBN: 1461371171 ISBN-13(EAN): 9781461371175
Издательство: Springer
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Цена: 20962.00 р.
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