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A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems, Hanif D. Sherali; W. P. Adams


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Автор: Hanif D. Sherali; W. P. Adams
Название:  A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems
ISBN: 9780792354871
Издательство: Springer
Классификация:


ISBN-10: 0792354877
Обложка/Формат: Hardcover
Страницы: 518
Вес: 0.94 кг.
Дата издания: 31.12.1998
Серия: Nonconvex Optimization and Its Applications
Язык: English
Размер: 234 x 156 x 30
Основная тема: Mathematics
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: Addresses a method for generating tight linear or convex programming relaxations for discrete and continuous nonconvex programming problems. This book is intended for researchers and practitioners who work in the area of discrete or continuous nonlinear, nonconvex optimization problems.


Reformulation: Nonsmooth, Piecewise Smooth, Semismooth and Smoothing Methods

Автор: Masao Fukushima; Liqun Qi
Название: Reformulation: Nonsmooth, Piecewise Smooth, Semismooth and Smoothing Methods
ISBN: 1441948058 ISBN-13(EAN): 9781441948052
Издательство: Springer
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Цена: 23058.00 р.
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Описание: The concept of "reformulation" has long been playing an important role in mathematical programming. A classical example is the penalization technique in constrained optimization that transforms the constraints into the objective function via a penalty function thereby reformulating a constrained problem as an equivalent or approximately equivalent unconstrained problem. More recent trends consist of the reformulation of various mathematical programming prob- lems, including variational inequalities and complementarity problems, into equivalent systems of possibly nonsmooth, piecewise smooth or semismooth nonlinear equations, or equivalent unconstrained optimization problems that are usually differentiable, but in general not twice differentiable. Because of the recent advent of various tools in nonsmooth analysis, the reformulation approach has become increasingly profound and diversified. In view of growing interests in this active field, we planned to organize a cluster of sessions entitled "Reformulation - Nonsmooth, Piecewise Smooth, Semismooth and Smoothing Methods" in the 16th International Symposium on Mathematical Programming (ismp97) held at Lausanne EPFL, Switzerland on August 24-29, 1997. Responding to our invitation, thirty-eight people agreed to give a talk within the cluster, which enabled us to organize thirteen sessions in total. We think that it was one of the largest and most exciting clusters in the symposium. Thanks to the earnest support by the speakers and the chairpersons, the sessions attracted much attention of the participants and were filled with great enthusiasm of the audience.

Utility Maximization in Nonconvex Wireless Systems

Автор: Johannes Brehmer
Название: Utility Maximization in Nonconvex Wireless Systems
ISBN: 3642443974 ISBN-13(EAN): 9783642443978
Издательство: Springer
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Цена: 15672.00 р.
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Описание: This book offers a framework for modeling and solving utility maximization problems in nonconvex wireless systems, presenting a general model for utility optimization in wireless systems, methods for solving problems, and case studies showing the model in use.

Duality Principles in Nonconvex Systems

Автор: David Yang Gao
Название: Duality Principles in Nonconvex Systems
ISBN: 1441948252 ISBN-13(EAN): 9781441948250
Издательство: Springer
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Цена: 25149.00 р.
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Nonsmooth/Nonconvex Mechanics

Автор: David Yang Gao; Raymond W. Ogden; Georgios E. Stav
Название: Nonsmooth/Nonconvex Mechanics
ISBN: 1461379733 ISBN-13(EAN): 9781461379737
Издательство: Springer
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Цена: 16769.00 р.
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Optimization on Low Rank Nonconvex Structures

Автор: Hiroshi Konno; Phan Thien Thach; Hoang Tuy
Название: Optimization on Low Rank Nonconvex Structures
ISBN: 0792343085 ISBN-13(EAN): 9780792343080
Издательство: Springer
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Цена: 36197.00 р.
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Описание: This work is devoted to global optimization problems with special structures. Most of these problems, though highly nonconvex, can be characterized by the property that they reduce to convex minimization problems when some of the variables are fixed.


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