Introduction to online convex optimization, second edition, Hazan, Elad

Автор: Stephen Boyd
Название: Convex Optimization
ISBN: 0521833787 ISBN-13(EAN): 9780521833783
Издательство: Cambridge Academ
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Цена: 119670.00 T
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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.

Автор: Zaslavski Alexander J.
Название: The Projected Subgradient Algorithm in Convex Optimization
ISBN: 3030602990 ISBN-13(EAN): 9783030602994
Издательство: Springer
Цена: 46570.00 T
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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.

Автор: Bonnans J Frederic
Название: Convex and Stochastic Optimization
ISBN: 3030149765 ISBN-13(EAN): 9783030149765
Издательство: Springer
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Цена: 55890.00 T
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Описание: This textbook provides an introduction to convex duality for optimization problems in Banach spaces, integration theory, and their application to stochastic programming problems in a static or dynamic setting. It introduces and analyses the main algorithms for stochastic programs, while the theoretical aspects are carefully dealt with.The reader is shown how these tools can be applied to various fields, including approximation theory, semidefinite and second-order cone programming and linear decision rules.This textbook is recommended for students, engineers and researchers who are willing to take a rigorous approach to the mathematics involved in the application of duality theory to optimization with uncertainty.

Автор: Nesterov Y.
Название: Introductory Lectures on Convex Optimization / A Basic Course
ISBN: 1402075537 ISBN-13(EAN): 9781402075537
Издательство: Springer
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Цена: 121110.00 T
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Описание: This is the first elementary exposition of the main ideas of complexity theory for convex optimization. Up to now, most of the material can be found only in special journals and research monographs. The book covers optimal methods and lower complexity bounds for smooth and non-smooth convex optimization. A separate chapter is devoted to polynomial-time interior-point methods. Audience: The book is suitable for industrial engineers and economists.

Автор: Hazan, Elad
Название: Introduction to online convex optimization
ISBN: 1680831704 ISBN-13(EAN): 9781680831702
Издательство: Неизвестно
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Цена: 151730.00 T
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Описание: Focuses on optimization as a process. This book is intended to serve as a reference for a self-contained course on online convex optimization and the convex optimization approach to machine learning for the educated graduate student in computer science/electrical engineering/operations research/statistics and related fields.

Автор: Nesterov
Название: Lectures on Convex Optimization
ISBN: 3319915770 ISBN-13(EAN): 9783319915777
Издательство: Springer
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Цена: 55890.00 T
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Описание: The first elementary exposition of core ideas of complexity theory for convex optimization, this book explores optimal methods and lower complexity bounds for smooth and non-smooth convex optimization. Also covers polynomial-time interior-point methods.

Автор: Nicolas Hadjisavvas; Panos M. Pardalos
Название: Advances in Convex Analysis and Global Optimization
ISBN: 0792369424 ISBN-13(EAN): 9780792369424
Издательство: Springer
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Цена: 167700.00 T
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Описание: A conference on Convex Analysis and Global Optimization was held in 2000 in Greece in honour of the memory of C. Caratheodory (1873-1950). This volume contains a selection of papers based on talks presented at the conference. The two themes of convexity and global optimization pervade the book.

Автор: Juditsky Anatoli, Nemirovski Arkadi
Название: Statistical Inference Via Convex Optimization
ISBN: 0691197296 ISBN-13(EAN): 9780691197296
Издательство: Wiley
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Цена: 92930.00 T
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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.

Название: Selected Applications of Convex Optimization
ISBN: 3662463555 ISBN-13(EAN): 9783662463550
Издательство: Springer
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Цена: 46570.00 T
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Описание: This book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter estimation, norm approximation and regularization, semi-definite programming problems, convex relaxation, and geometric problems.

Автор: Sorin-Mihai Grad
Название: Vector Optimization and Monotone Operators via Convex Duality
ISBN: 3319088998 ISBN-13(EAN): 9783319088990
Издательство: Springer
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Цена: 102480.00 T
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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.

Автор: Pardalos Panos M., Zilinskas Antanas, Zilinskas Julius
Название: Non-Convex Multi-Objective Optimization
ISBN: 3319869817 ISBN-13(EAN): 9783319869810
Издательство: Springer
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Цена: 93160.00 T
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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.

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: 139750.00 T
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