9780691091938 - self-regularity: a new paradigm for primal-dual interior-point algorithms: 22 (princeton series in applied mathematics) de peng, jimine; roos, cornelis; terlaky, tamas (16 resultados)
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Serie: Princeton Series in Applied Mathematics, Libro 3 de 33. Libro 3 de 33 - Princeton Series in Applied Mathematics
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Editorial: Princeton University Press, Princeton, New Jersey, U. S. A., 2002
Serie: Princeton Series in Applied Mathematics, Libro 3 de 33. Libro 3 de 33 - Princeton Series in Applied Mathematics
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Paperback. Condición: New. First Edition. Paperback in as-new condition: minor shelfwear only: contents clean, sound, bright. Used.
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Editorial: Princeton University Press, 2002
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Condición: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
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Editorial: Princeton Univ Pr, Princeton, New Jersey, U.S.A., 2002
Serie: Princeton Series in Applied Mathematics, Libro 3 de 33. Libro 3 de 33 - Princeton Series in Applied Mathematics
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paperback. Condición: New. In shrink wrap. Looks like an interesting title.
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Paperback. Condición: 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 betw…een 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.
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Editorial: Princeton University Press, 2002
Serie: Princeton Series in Applied Mathematics, Libro 3 de 33. Libro 3 de 33 - Princeton Series in Applied Mathematics
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Idioma: Inglés
Editorial: Princeton University Press, 2002
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Paperback. Condición: 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 betw…een 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.
Idioma: Inglés
Editorial: Princeton Univ Pr, 2002
Serie: Princeton Series in Applied Mathematics, Libro 3 de 33. Libro 3 de 33 - Princeton Series in Applied Mathematics
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Paperback. Condición: Brand New. illustrated edition. 208 pages. 9.00x6.00x0.50 inches. In Stock.
Idioma: Inglés
Editorial: Princeton University Press, 2002
Serie: Princeton Series in Applied Mathematics, Libro 3 de 33. Libro 3 de 33 - Princeton Series in Applied Mathematics
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Condición: 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.
Idioma: Inglés
Editorial: Princeton University Press, 2002
Serie: Princeton Series in Applied Mathematics, Libro 3 de 33. Libro 3 de 33 - Princeton Series in Applied Mathematics
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Taschenbuch. Condición: Neu. Self-Regularity | A New Paradigm for Primal-Dual Interior-Point Algorithms | Jiming Peng (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2002 | Princeton University Press | EAN 9780691091938 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]li…bri[dot]de | Anbieter: preigu Print on Demand.
Idioma: Inglés
Editorial: Princeton University Press, 2002
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Taschenbuch. Condición: 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.
