Advances in Convex Analysis and Global Optimization, Nicolas Hadjisavvas; Panos M. Pardalos

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.

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: 18167.00 р.
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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: 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.

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.

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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Название: Selected Applications of Convex Optimization
ISBN: 3662463555 ISBN-13(EAN): 9783662463550
Издательство: Springer
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Цена: 6986.00 р.
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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: 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.

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: 15372.00 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.

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.

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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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.