A Unified approach to interior point algorithms for linear complementarity problems

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-po...

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Auteurs principaux: Kojima, Masakazu, 1947-, Megiddo, Nimrod, 19..- (Auteur), Noma, Toshihito, 19..- (Auteur)
Format: Livre numérique
Langue:Anglais
Publié: Berlin [etc.] : Springer [20..].
Cham : Springer Nature
Collection:Lecture notes in computer science 538
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Edition sous un autre format:• A Unified approach to interior point algorithms for linear complementarity problems, M. Kojima,... N. Megiddo,... T. Noma,... [et al.], Berlin, Springer-Verlag, 1991, 1 vol. (VIII-108 p.), Lecture notes in computer science, 0-387-54509-3
• A Unified Approach to Interior Point Algorithms for Linear Complementarity Problems, Texte imprimé, 9783662207840
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Résumé: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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Archives Springer e-books (Licence nationale)
ISBN:9783540384267 (PDF)
ISSN:1611-3349
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