The Projected Subgradient Algorithm in Convex Optimization, Zaslavski Alexander J.
Автор: Stephen Boyd Название: Convex Optimization ISBN: 0521833787 ISBN-13(EAN): 9780521833783 Издательство: Cambridge Academ Рейтинг: Цена: 17950.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Boyd Stephen, Busseti Enzo, Diamond Steven Название: Multi-Period Trading Via Convex Optimization ISBN: 1680833286 ISBN-13(EAN): 9781680833287 Издательство: Неизвестно Рейтинг: Цена: 8966.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Miroslav Bacak Название: Convex analysis and optimization in Hadamard spaces ISBN: 3110361035 ISBN-13(EAN): 9783110361032 Издательство: Walter de Gruyter Рейтинг: Цена: 20446.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In the past two decades, convex analysis and optimization have been developed in Hadamard spaces. This book represents a first attempt to give a systematic account on the subject. Hadamard spaces are complete geodesic spaces of nonpositive curvature. They include Hilbert spaces, Hadamard manifolds, Euclidean buildings and many other important spaces. While the role of Hadamard spaces in geometry and geometric group theory has been studied for a long time, first analytical results appeared as late as in the 1990s. Remarkably, it turns out that Hadamard spaces are appropriate for the theory of convex sets and convex functions outside of linear spaces. Since convexity underpins a large number of results in the geometry of Hadamard spaces, we believe that its systematic study is of substantial interest. Optimization methods then address various computational issues and provide us with approximation algorithms which may be useful in sciences and engineering. We present a detailed description of such an application to computational phylogenetics. The book is primarily aimed at both graduate students and researchers in analysis and optimization, but it is accessible to advanced undergraduate students as well.
Автор: Juditsky Anatoli, Nemirovski Arkadi Название: Statistical Inference Via Convex Optimization ISBN: 0691197296 ISBN-13(EAN): 9780691197296 Издательство: Wiley Рейтинг: Цена: 13939.00 р. Наличие на складе: Поставка под заказ.
Описание:
This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory can be used to devise and analyze near-optimal statistical inferences.
Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems--sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals--demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems.
Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text.
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.
Автор: Aram Arutyunov, Valeri Obukhovskii Название: Convex and Set-Valued Analysis ISBN: 3110460289 ISBN-13(EAN): 9783110460285 Издательство: Walter de Gruyter Рейтинг: Цена: 11148.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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
Автор: Borwein, Jonathan M. Lewis, Adrian S. (university Of Waterloo) Название: Convex analysis and nonlinear optimization ISBN: 1441921273 ISBN-13(EAN): 9781441921277 Издательство: Springer Рейтинг: Цена: 8378.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Roman G. Strongin; Yaroslav D. Sergeyev Название: Global Optimization with Non-Convex Constraints ISBN: 0792364902 ISBN-13(EAN): 9780792364900 Издательство: Springer Рейтинг: Цена: 43184.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Juan Peypouquet Название: Convex Optimization in Normed Spaces ISBN: 3319137093 ISBN-13(EAN): 9783319137094 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Brinkhuis Jan Название: Convex Analysis for Optimization: A Unified Approach ISBN: 3030418030 ISBN-13(EAN): 9783030418038 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Roman G. Strongin; Yaroslav D. Sergeyev Название: Global Optimization with Non-Convex Constraints ISBN: 1461371171 ISBN-13(EAN): 9781461371175 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Nisheeth K. Vishnoi Название: Algorithms for Convex Optimization ISBN: 1108482023 ISBN-13(EAN): 9781108482028 Издательство: Cambridge Academ Рейтинг: Цена: 13147.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Algorithms for Convex Optimization are the workhorses of data-driven, technological advancements in machine learning and artificial intelligence. This concise, modern guide to deriving these algorithms is self-contained and accessible to advanced students, practitioners, and researchers in computer science, operations research, and data science.
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