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A Unified Approach to Interior Point Algorithms for Linear Complementarity Problems (Lecture Notes in Computer Science) ePub download

by M. Kojima,N. Megiddo,T. Noma,A. Yoshise

  • Author: M. Kojima,N. Megiddo,T. Noma,A. Yoshise
  • ISBN: 0387545093
  • ISBN13: 978-0387545097
  • ePub: 1350 kb | FB2: 1232 kb
  • Language: English
  • Category: Mathematics
  • Publisher: Springer-Verlag (December 1991)
  • Pages: 108
  • Rating: 4.5/5
  • Votes: 558
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A Unified Approach to Interior Point Algorithms for Linear Complementarity Problems (Lecture Notes in Computer Science) ePub download

Following Karmarkar's 1984 linear programming algorithm, numerous interior-point algorithms have been proposed for various mathematical programming .

Following Karmarkar's 1984 linear programming algorithm, numerous interior-point algorithms have been proposed for various mathematical programming problems such as linear programming, convex quadratic programming and convex programming in general. This monograph presents. eBook 51,16 €. price for Russian Federation (gross). ISBN 978-3-540-38426-7.

Masakazu Kojima, Nimrod Megiddo, +1 author Akiko Yoshise. Published in Lecture Notes in Computer Science 1991. Now welcome, the most inspiring book today from a very professional writer in the world, a unified approach to interior point algorithms for linear complementarity problems lecture notes in computer science vol 538. This is the book that many people in the world waiting for to publish. After the announced of this book, the book lovers are really curious to see how this book is actually. Are you one of them? That's very proper. You may not be regret now.

KEYWORDS: Linear Complementarity Problem, Nonlinear Penalized Equation, Newton Method, Singular Values. JOURNAL NAME: Applied Mathematics, Vo. N. 4, December 31, 2015. ABSTRACT: The existence condition of the solution of special nonlinear penalized equation of the linear complementarity problems is obtained by the relationship between penalized equations and an absolute value equation. Newton method is used to solve penalized equation, and then the solution of the linear complementarity problems is obtained.

A large class of potential reduction algorithms is presented in a unified way.

1991 Серия: Lecture Notes in Computer Science Язык: ENG Размер: 2. 9 x 1. 0 x . 6 cm Основная тема: Mathematics Рейтинг: Поставляется из: Германии Описание: This monograph presents a study of interior-point algorithms for the linear complementarity problem, known as a mathematical model for primal-dual pairs of linear programs and convex quadratic programs. A large class of potential reduction algorithms is presented in a unified way.

ISBN13: 9780387545097. More Books . ABOUT CHEGG.

A unified approach to interior point algorithms for linear complementarity problems: A summary. This note summarizes a report with the same title, where a study was carried out regarding a unified approach, proposed by Kojima, Mizuno and Yoshise, for interior point algorithms for the linear complementarily problem with a positive semi-definite matrix. Different from most existing interior-point algorithms that are based on the central path, this algorithm tracks the regularized central path which exists for any continuous P problem.

M. Kojima, N. Megiddo, T. Noma, A. Yoshise, A Unified Approach to Interior Point Algorithms for Linear Complementarity Problems, Lecture Notes in Computer Science, Vol. 538, Springer-Verlag, New York, (1991). Y. Zhang, On the convergence of a class of infeasible interior-point methods for the horizontal linear complementarity problem, SIAM J. Optim, 4(1)(1994), p. 08-227. C. Roos, T. Terlaky, J. -Ph. Vial, Theory and Algorithms for Linear Optimization. An Interior-Point Approach.

Kojima, N. Noma and A. Yoshise (1991) "A Unified Approach to Interior Point Algorithms for Linear Complementarity Problems", Lecture Notes in Computer Science, Vol. 538, Springer-Verlag. M. Megiddo and S. Mizuno (1993) "Theoretical Convergence of Large-Step Primal-Dual Interior Point Algorithms for Linear Programming", Mathematical Programming, Vol. 59, 1-22. Mizuno (1993) "A Primal-Dual Point Algorithm for Linear Programming", Mathematical Programming, Vol. 61, 263-280. Noma, and A. Yoshise, A. unified approach to interior point algorithms for linear. complementarity problems, volume 538 of Lecture Notes. in Computer Science, 1991

M. in Computer Science, 1991. Z. Darvay, A ing method for linear. optimization, Studia Universitatis Babes-Bolyai, Series. In this paper, we present a full-Newton feasible step interior-point algorithm for solving monotone horizontal linear complementarity problems. Yoshise, "A unified approach to interior point algorithms for linear complementarity problems," Lecture Notes in Computer Science 538, Springer-Verlag, 1991. Journal Articles Book Chapters. Megiddo, "Pathways to the optimal set in linear programming" in: Progress in Mathematical Programming: Interior-Point and Related Methods (N. Megiddo, E. Springer-Verlag, New York, 1988, pp. 131-158.

Following Karmarkar's 1984 linear programming algorithm, numerous interior-point algorithms have been proposed for various mathematical programming problems such as linear programming, convex quadratic programming and convex programming in general. This monograph presents a study of interior-point algorithms for the linear complementarity problem (LCP) which is known as a mathematical model for primal-dual pairs of linear programs and convex quadratic programs. A large family of potential reduction algorithms is presented in a unified way for the class of LCPs where the underlying matrix has nonnegative principal minors (P0-matrix). This class includes various important subclasses such as positive semi-definite matrices, P-matrices, P*-matrices introduced in this monograph, and column sufficient matrices. The family contains not only the usual potential reduction algorithms but also path following algorithms and a damped Newton method for the LCP. The main topics are global convergence, global linear convergence, and the polynomial-time convergence of potential reduction algorithms included in the family.
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