Algorithms for convex optimization /, Vishnoi, Nisheeth K.,

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: 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.

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.

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: 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: 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: 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: Alexander M. Rubinov; Xiao-qi Yang
Название: Lagrange-type Functions in Constrained Non-Convex Optimization
ISBN: 1461348218 ISBN-13(EAN): 9781461348214
Издательство: Springer
Рейтинг:
Цена: 13974.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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.

Автор: Panos M. Pardalos; Antanas ?ilinskas; Julius ?ilin
Название: Non-Convex Multi-Objective Optimization
ISBN: 3319610058 ISBN-13(EAN): 9783319610054
Издательство: Springer
Рейтинг:
Цена: 12577.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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.

Автор: Brinkhuis Jan
Название: Convex Analysis for Optimization: A Unified Approach
ISBN: 3030418065 ISBN-13(EAN): 9783030418069
Издательство: Springer
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: 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.