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Published by Princeton University Press, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Published by Princeton University Press, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Published by Princeton University Press, Princeton, New Jersey, U. S. A., 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Published by Princeton Univ Pr, Princeton, New Jersey, U.S.A., 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Published by Princeton University Press, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Published by Princeton University Press, 2002
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Published by Princeton University Press, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Published by Princeton University Press, 2002
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Published by Princeton University Press, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Published by Princeton University Press, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Published by Princeton University Press, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Paperback / softback. Condition: New. New copy - Usually dispatched within 4 working days. 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.
Published by Princeton University Press, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Published by Princeton University Press 2002-12-06, Princeton |Oxford, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Published by Princeton University Press, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Published by Princeton Univ Pr, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Published by Princeton Univ Pr, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Published by Princeton University Press, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Published by Princeton University Press, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
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Taschenbuch. Condition: 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.
Published by Princeton University Press, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
Seller: Kennys Bookstore, Olney, MD, U.S.A.
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Condition: 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.
Published by Princeton University Press, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Book
Condition: 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. . . . .
Published by Princeton University Press, New Jersey, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
Seller: CitiRetail, Stevenage, United Kingdom
Book
Paperback. Condition: new. Paperback. Research on interior-point methods (IPMs) has dominated the field of mathematical programming since the 1980s. 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 neighbourhood of the central path until an approximate solution to the problem is f 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Published by Princeton University Press, New Jersey, 2002
ISBN 10: 0691091935ISBN 13: 9780691091938
Seller: AussieBookSeller, Truganina, VIC, Australia
Book
Paperback. Condition: new. Paperback. Research on interior-point methods (IPMs) has dominated the field of mathematical programming since the 1980s. 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 neighbourhood of the central path until an approximate solution to the problem is f 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.