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ISBN 10 : 0691091935 ISBN 13 : 9780691091938
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ISBN 10 : 0691091927 ISBN 13 : 9780691091921
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Edité par Princeton University Press, USA, 2002
ISBN 10 : 0691091927 ISBN 13 : 9780691091921
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Edité par Princeton Univ Pr, Princeton, New Jersey, U.S.A., 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
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Edité par Princeton University Press (edition ), 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
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Edité par Princeton University Press, 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
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Edité par Princeton University Press, 2002
ISBN 10 : 0691091927 ISBN 13 : 9780691091921
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Edité par Princeton University Press, 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
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Edité par Princeton University Press, 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
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Edité par Princeton University Press, 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
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Ajouter au panierEtat : New. Presents a framework for the theory of primal-dual IPMs based on the notion of the self-regularity of a function. This book deals with linear optimization, nonlinear complementarity problems, semidefinite optimization, and second-order conic optimization problems. Series: Princeton Series in Applied Mathematics. Num Pages: 208 pages, 1, black & white illustrations. BIC Classification: PBU; PBW. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 229 x 152 x 11. Weight in Grams: 28. . 2002. Paperback. . . . .
Edité par Princeton University Press, 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
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Edité par Princeton Univ Pr, Princeton, New Jersey, 2002
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Ajouter au panierLaminated Boards. Etat : New. Copyright 2002, no edition stated. 8vo. Laminated boards, 185 pp. Part of Princeton Series in Applied Mathematics. Linear optimization, nonlinear complementarity problems, semidefinite optimization, and second-order conic optimization problems. New.
Edité par Princeton University Press, 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
Langue: anglais
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Edité par Princeton University Press, 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
Langue: anglais
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Edité par Princeton University Press, 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
Langue: anglais
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Ajouter au panierEtat : New. Presents a framework for the theory of primal-dual IPMs based on the notion of the self-regularity of a function. This book deals with linear optimization, nonlinear complementarity problems, semidefinite optimization, and second-order conic optimization problems. Series: Princeton Series in Applied Mathematics. Num Pages: 208 pages, 1, black & white illustrations. BIC Classification: PBU; PBW. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 229 x 152 x 11. Weight in Grams: 28. . 2002. Paperback. . . . . Books ship from the US and Ireland.
Edité par Princeton University Press, 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
Langue: anglais
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Edité par Princeton University Press, 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
Langue: anglais
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Edité par Princeton University Press, US, 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
Langue: anglais
Vendeur : Rarewaves USA, OSWEGO, IL, Etats-Unis
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Ajouter au panierPaperback. Etat : New. Research on interior-point methods (IPMs) has dominated the field of mathematical programming for the last two decades. Two contrasting approaches in the analysis and implementation of IPMs are the so-called small-update and large-update methods, although, until now, there has been a notorious gap between the theory and practical performance of these two strategies. This book comes close to bridging that gap, presenting a new framework for the theory of primal-dual IPMs based on the notion of the self-regularity of a function. The authors deal with linear optimization, nonlinear complementarity problems, semidefinite optimization, and second-order conic optimization problems. The framework also covers large classes of linear complementarity problems and convex optimization. The algorithm considered can be interpreted as a path-following method or a potential reduction method. Starting from a primal-dual strictly feasible point, the algorithm chooses a search direction defined by some Newton-type system derived from the self-regular proximity.The iterate is then updated, with the iterates staying in a certain neighborhood of the central path until an approximate solution to the problem is found. By extensively exploring some intriguing properties of self-regular functions, the authors establish that the complexity of large-update IPMs can come arbitrarily close to the best known iteration bounds of IPMs. Researchers and postgraduate students in all areas of linear and nonlinear optimization will find this book an important and invaluable aid to their work.
Edité par Princeton University Press, US, 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
Langue: anglais
Vendeur : Rarewaves USA United, OSWEGO, IL, Etats-Unis
EUR 113,82
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Ajouter au panierPaperback. Etat : New. Research on interior-point methods (IPMs) has dominated the field of mathematical programming for the last two decades. Two contrasting approaches in the analysis and implementation of IPMs are the so-called small-update and large-update methods, although, until now, there has been a notorious gap between the theory and practical performance of these two strategies. This book comes close to bridging that gap, presenting a new framework for the theory of primal-dual IPMs based on the notion of the self-regularity of a function. The authors deal with linear optimization, nonlinear complementarity problems, semidefinite optimization, and second-order conic optimization problems. The framework also covers large classes of linear complementarity problems and convex optimization. The algorithm considered can be interpreted as a path-following method or a potential reduction method. Starting from a primal-dual strictly feasible point, the algorithm chooses a search direction defined by some Newton-type system derived from the self-regular proximity.The iterate is then updated, with the iterates staying in a certain neighborhood of the central path until an approximate solution to the problem is found. By extensively exploring some intriguing properties of self-regular functions, the authors establish that the complexity of large-update IPMs can come arbitrarily close to the best known iteration bounds of IPMs. Researchers and postgraduate students in all areas of linear and nonlinear optimization will find this book an important and invaluable aid to their work.
Edité par Princeton University Press, 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
Langue: anglais
Vendeur : Best Price, Torrance, CA, Etats-Unis
EUR 95,14
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Edité par Princeton University Press, 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 80,92
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents a framework for the theory of primal-dual IPMs based on the notion of the self-regularity of a function. This book deals with linear optimization, nonlinear complementarity problems, semidefinite optimization, and second-order conic optimization pr.
Edité par Princeton University Press, 2002
ISBN 10 : 0691091935 ISBN 13 : 9780691091938
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 98,70
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Ajouter au panierTaschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Research on interior-point methods (IPMs) has dominated the field of mathematical programming for the last two decades. Two contrasting approaches in the analysis and implementation of IPMs are the so-called small-update and large-update methods, although, until now, there has been a notorious gap between the theory and practical performance of these two strategies. This book comes close to bridging that gap, presenting a new framework for the theory of primal-dual IPMs based on the notion of the self-regularity of a function.The authors deal with linear optimization, nonlinear complementarity problems, semidefinite optimization, and second-order conic optimization problems. The framework also covers large classes of linear complementarity problems and convex optimization. The algorithm considered can be interpreted as a path-following method or a potential reduction method. Starting from a primal-dual strictly feasible point, the algorithm chooses a search direction defined by some Newton-type system derived from the self-regular proximity. The iterate is then updated, with the iterates staying in a certain neighborhood of the central path until an approximate solution to the problem is found. By extensively exploring some intriguing properties of self-regular functions, the authors establish that the complexity of large-update IPMs can come arbitrarily close to the best known iteration bounds of IPMs.Researchers and postgraduate students in all areas of linear and nonlinear optimization will find this book an important and invaluable aid to their work.