Nonlinear and Convex Analysis in Economic Theory, Toru Maruyama; Wataru Takahashi

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

Название: Selected Applications of Convex Optimization
ISBN: 3662463555 ISBN-13(EAN): 9783662463550
Издательство: Springer
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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: 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: 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: 25155.00 р.
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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: J. Kriens
Название: Convex Analysis and Mathematical Economics
ISBN: 3540092471 ISBN-13(EAN): 9783540092476
Издательство: Springer
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Цена: 15372.00 р.
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Описание: On February 20, 1978, the Department of Econometrics of the University of Tilburg organized a symposium on Convex Analysis and Mathematical th Economics to commemorate the 50 anniversary of the University.

Автор: Jean-Baptiste Hiriart-Urruty; Claude Lemarechal
Название: Convex Analysis and Minimization Algorithms I
ISBN: 3642081614 ISBN-13(EAN): 9783642081613
Издательство: Springer
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Описание: This book covers the fundamentals of convex analysis, a refinement of standard calculus with equalities and approximations replaced by inequalities. Reviews minimization algorithms, which provide immediate application to optimization and operations research.

Автор: D. den Hertog
Название: Interior Point Approach to Linear, Quadratic and Convex Programming
ISBN: 0792327349 ISBN-13(EAN): 9780792327349
Издательство: Springer
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Цена: 13275.00 р.
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Описание: This text describes the rapidly developing field of interior point methods (IPMs). An extensive analysis is given of path-following methods for linear programming, quadratic programming and convex programming.

Автор: M.J. Panik
Название: Fundamentals of Convex Analysis
ISBN: 9048142717 ISBN-13(EAN): 9789048142712
Издательство: Springer
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Цена: 23751.00 р.
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Описание: Fundamentals of Convex Analysis offers an in-depth look at some of the fundamental themes covered within an area of mathematical analysis called convex analysis.

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.