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One has to make everything as simple as possible but, never more simple. Albert Einstein Discovery consists of seeing what every body has seen and thinking what nobody has thought. Albert S. ent_Gyorgy; The primary goal of this book is to provide an introduction to the theory of Interior Point Methods (IPMs) in Mathematical Programming. At the same time, we try to present a quick overview of the impact of extensions of IPMs on smooth nonlinear optimization and to demonstrate the potential of IPMs for solving difficult practical problems. The Simplex Method has dominated the theory and practice of mathematical pro gramming since 1947 when Dantzig discovered it. In the fifties and sixties several attempts were made to develop alternative solution methods. At that time the prin cipal base of interior point methods was also developed, for example in the work of Frisch (1955), Caroll (1961), Huard (1967), Fiacco and McCormick (1968) and Dikin (1967). In 1972 Klee and Minty made explicit that in the worst case some variants of the simplex method may require an exponential amount of work to solve Linear Programming (LP) problems. This was at the time when complexity theory became a topic of great interest. People started to classify mathematical programming prob lems as efficiently (in polynomial time) solvable and as difficult (NP-hard) problems. For a while it remained open whether LP was solvable in polynomial time or not. The break-through resolution ofthis problem was obtained by Khachijan (1989).
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Contenuti
Preface. Part I: Linear Programming. 1. Introduction to the Theory of Interior Point Methods; B. Jansen, et al. 2. Affine Scaling Algorithm; T. Tsuchiya. 3. Target-Following Methods for Linear Programming; B. Jansen, et al. 4. Potential Reduction Algorithms; K.M. Anstreicher. 5. Infeasible-Interior-Point Algorithms; S. Mizuno. 6. Implementation of Interior-Point Methods for Large Scale Linear Programs; E.D. Andersen, et al. Part II: Convex Programming. 7. Interior-Point Methods for Classes of Convex Programs; F. Jarre. 8. Complementarity Problems; A. Yoshise. 9. Semidefinite Programming; M.V. Ramana, P.M. Pardalos. 10. Implementing Barrier Methods for Nonlinear Programming; D.F. Shanno, et al. Part III: Applications, Extensions. 11. Interior Point Methods for Combinatorial Optimization; J.E. Mitchell. 12. Interior Point Methods for Global Optimization; P.M. Pardalos, M.G.C. Resende. 13. Interior Point Approaches for the VLSI Placement Problem; A. Vannelli, et al.
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Interior Point Methods in Mathematical Programming Terlaky, Tamas
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Hardcover Sep 30, 1996. Condizione: Used: Very Good. Interior Point Methods in Mathematical Programming | T. Terlaky | Kluwer Academic, 1996, in-8 cartonnage éditeur, 528 pages. Couverture très légèrement passée. Dos solide. Intérieur frais. Exemplaire de bibliothèque : petit code barre en pied de 1re de couv., cotation au dos, rares et discrets petits tampons à l'intérieur de l'ouvrage. Bon exemplaire. [BT35+]. Codice articolo 0705UNHHY61
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Interior Point Methods in Mathematical Programming Terlaky, Tamas
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Hardcover Sep 30, 1996. Condizione: Used: Very Good. Interior Point Methods in Mathematical Programming | T. Terlaky | Kluwer Academic, 1996, in-8 cartonnage éditeur, 528 pages. Couverture très légèrement passée. Dos solide. Intérieur frais. Exemplaire de bibliothèque : petit code barre en pied de 1re de couv., cotation au dos, rares et discrets petits tampons à l'intérieur de l'ouvrage. Bon exemplaire. [BT35+]. Codice articolo 0705UN2VQBX
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Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 556 | Sprache: Englisch | Produktart: Bücher | One has to make everything as simple as possible but, never more simple. Albert Einstein Discovery consists of seeing what every body has seen and thinking what nobody has thought. Albert S. ent_Gyorgy; The primary goal of this book is to provide an introduction to the theory of Interior Point Methods (IPMs) in Mathematical Programming. At the same time, we try to present a quick overview of the impact of extensions of IPMs on smooth nonlinear optimization and to demonstrate the potential of IPMs for solving difficult practical problems. The Simplex Method has dominated the theory and practice of mathematical pro gramming since 1947 when Dantzig discovered it. In the fifties and sixties several attempts were made to develop alternative solution methods. At that time the prin cipal base of interior point methods was also developed, for example in the work of Frisch (1955), Caroll (1961), Huard (1967), Fiacco and McCormick (1968) and Dikin (1967). In 1972 Klee and Minty made explicit that in the worst case some variants of the simplex method may require an exponential amount of work to solve Linear Programming (LP) problems. This was at the time when complexity theory became a topic of great interest. People started to classify mathematical programming prob lems as efficiently (in polynomial time) solvable and as difficult (NP-hard) problems. For a while it remained open whether LP was solvable in polynomial time or not. The break-through resolution ofthis problem was obtained by Khachijan (1989). Codice articolo 1922532/202
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Condizione: New. Offers an overview of the research in interior point methods. This book gives an overview of basic variants of interior point algorithms for linear programming. It also deals with nonlinear programming. It is useful for researchers and graduate students in mathematical programming, operations research, management science, and combinatorics. Editor(s): Terlaky, Tamas. Series: Applied Optimization. Num Pages: 552 pages, biography. BIC Classification: PBT; PBW; UM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 30. Weight in Grams: 951. . 1996. Hardback. . . . . Codice articolo V9780792342014
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Condizione: New. Offers an overview of the research in interior point methods. This book gives an overview of basic variants of interior point algorithms for linear programming. It also deals with nonlinear programming. It is useful for researchers and graduate students in mathematical programming, operations research, management science, and combinatorics. Editor(s): Terlaky, Tamas. Series: Applied Optimization. Num Pages: 552 pages, biography. BIC Classification: PBT; PBW; UM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 30. Weight in Grams: 951. . 1996. Hardback. . . . . Books ship from the US and Ireland. Codice articolo V9780792342014
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