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Titlebook: Optimization Theory; Recent Developments Franco Giannessi,Panos Pardalos,Tamás Rapcsák Book 20011st edition Kluwer Academic Publishers 200

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书目名称Optimization Theory
副标题Recent Developments
编辑Franco Giannessi,Panos Pardalos,Tamás Rapcsák
视频video
丛书名称Applied Optimization
图书封面Titlebook: Optimization Theory; Recent Developments  Franco Giannessi,Panos Pardalos,Tamás Rapcsák Book 20011st edition Kluwer Academic Publishers 200
描述This volume contains refereed papers based on the lectures presented at the XIV International Conference on Mathematical Programming held at Matrahaza, Hungary, between 27-31 March 1999. This conference was organized by the Laboratory of Operations Research and Deci­ sion Systems at the Computer and Automation Institute, Hungarian Academy of Sciences. The editors hope this volume will contribute to the theory and applications of mathematical programming. As a tradition of these events, the main purpose of the confer­ ence was to review and discuss recent advances and promising research trends concerning theory, algorithms and applications in different fields of Optimization Theory and related areas such as Convex Analysis, Complementarity Systems and Variational Inequalities. The conference is traditionally held in the Matra Mountains, and housed by the resort house of the Hungarian Academy of Sciences. This was the 14th event of the long lasting series of conferences started in 1973. The organizers wish to express their thanks to the authors for their contributions in this volume, and the anonymous referees for their valu­ able comments. Special thanks are directed to our sponsors
出版日期Book 20011st edition
关键词algorithms; Approximation; calculus; computer; inequality; Mathematica; operations research; optimization; p
版次1
doihttps://doi.org/10.1007/978-1-4613-0295-7
isbn_softcover978-1-4613-7983-6
isbn_ebook978-1-4613-0295-7Series ISSN 1384-6485
issn_series 1384-6485
copyrightKluwer Academic Publishers 2001
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Heuristics for the Process Network Synthesis Problem,find a suitable network of operating units which produces the desired products from the given raw materials. By considering this network design from a structural point of view, we obtain a combinatorial optimization problem called the Process Network Synthesis or (PNS) problem. It is known that the
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Heuristics for Simplified Process Network Synthesis (PNS) Problems with a Blossom-Type Algorithm foch the total number of input and output materials is at most 3. This observation facilitates useful application of the Edge Covering Problem of weighted graphs, which can be used to develop a new heuristic procedure for the PNS problem.
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The Linear Complementarity Problem and the Class of Generalized Positive Subdefinite Matrices,e solution set to a given LCP always contains the KKT-set of the corresponding quadratic programming problem. The reverse implication holds only for some very special classes of matrices as positive semidefinite, P-, row adequate, row sufficient and positive subdefinite (.)matrices. The paper extend
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Computer Experiences with Successive Regression Approximations for Solving Equations,r-ministic function values. Here we apply this method to noisy function values. A set of Fortran subroutines, performing the actual computations has been developed, among them a subroutine, capable of computing the gradient of the multidimen-sional normal distribution. A short description of the met
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A Bundle of Smooth Lagrangians in Mathematical Programming,ity problem is stated and sufficient conditions for the zero duality gap are formulated. In the case of smooth mathematical programming problems this general approach leads to Exact Nonsmooth Penalty functions. It is shown that a smooth Lagrange function exists in the case where the so-called ‘abstr
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Duality For D.C. Optimization Over Compact Sets,0, 8, 3, 12, 11, 4] and references therein). This paper contains an alternative approach to the duality theory for d.c. programming problems developed in [6]. That theory associates to the general problem of minimizing a d.c. function under several d.c. constraints on an arbitrary locally convex spa
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