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Tuesday, May 12, 2020 | History

5 edition of reformulation-linearization technique for solving discrete and continuous nonconvex problems found in the catalog.

reformulation-linearization technique for solving discrete and continuous nonconvex problems

by Hanif D. Sherali

  • 313 Want to read
  • 3 Currently reading

Published by Kluwer Academic in Dordrecht, Boston, Mass .
Written in English

    Subjects:
  • Nonconvex programming

  • Edition Notes

    Includes bibliographical references (p. 493-514).

    Statementby Hanif D. Sherali and Warren P. Adams.
    SeriesNonconvex optimization and its applications ;, v. 31
    ContributionsAdams, Warren P.
    Classifications
    LC ClassificationsT57.817 .S43 1999
    The Physical Object
    Paginationxxiii, 514 p. :
    Number of Pages514
    ID Numbers
    Open LibraryOL384844M
    ISBN 100792354877
    LC Control Number98047439

    A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems (Nonconvex Optimization and Its Applications (closed)) by Hanif D. Sherali, W.P. Adams1/5(1). This paper presents an effective optimization method for solving general constrained nonconvex mixed discrete-continuous programming problems. Well-cited test cases from various disciplines are used to evaluate the efficacy of the proposed method. The results are shown to be in agreement or better than those reported in the optimization by: 9.

    Discrete and continuous nonconvex programming problems arise in a host of practical applications in the context of production planning and control, location-allocation, distribution, economics and game theory, quantum chemistry, and process and engineering design situations. Several recent advances have been made in the development of branch-and-cut type algorithms for mixed-integer linear and. The results of this study are expected to lead to more efficient tools for solving a variety of challenging nonconvex optimization programs, both discrete and continuous. The discrete programs arise in such diverse areas as clustering, cryptography, facility layout, logical inference, and scheduling, while the continuous nonconvex programs have.

    Reformulation‐Linearization Technique for MIPs. Hanif D. Sherali. Virginia Polytechnic Institute and State University, Grado Department of Industrial and Systems Engineering, Blacksburg, Virginia. Search for more papers by this author. Hanif D. by: 1. A reformulation-linearization technique for solving discrete and continuous nonconvex problems By Hanif D. Sherali and Warren P. Adams. Kluwer Academic Publishers, Dordrecht. (). pages. $, NLG , GBP


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Reformulation-linearization technique for solving discrete and continuous nonconvex problems by Hanif D. Sherali Download PDF EPUB FB2

This book deals with the theory and applications of the Reformulation- Linearization/Convexification Technique (RL T) for solving nonconvex optimization problems. A unified treatment of discrete and continuous nonconvex programming problems is presented using this by: For example, the binariness on a variable x.

can be equivalently J expressed as the polynomial constraint x. (1-x.) = 0. The motivation for this book is J J the role of tight linear/convex programming representations or relaxations in solving such discrete and continuous nonconvex programming : $ Introduction This book deals with the theory and applications of the Reformulation- Linearization/Convexification Technique (RL T) for solving nonconvex optimization problems.

A unified treatment of discrete and continuous nonconvex programming problems is. A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems. A 'read' is counted each time someone views a publication summary (such as the title.

Overview This book deals with the theory and applications of the Reformulation- Linearization/Convexification Technique (RL T) for solving nonconvex optimization problems.

A unified treatment of discrete and continuous nonconvex programming problems is Pages: Description This book deals with the theory and applications of the Reformulation- Linearization/Convexification Technique (RL T) for solving nonconvex optimization problems.

A unified treatment of discrete and continuous nonconvex programming problems is. A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems Hanif D. Sherali, Warren P.

Adams (auth.) This book deals with the theory and applications of the Reformulation- Linearization/Convexification Technique (RL T) for solving nonconvex optimization problems. A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems by Hanif D.

Sherali, W. Adams Paperback Book, pages See Other Available Editions Description This book addresses a new method for generating tight linear or convex programming relaxations for discrete and continuous nonconvex programming : Note: If you're looking for a free download links of A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems (Nonconvex Optimization and Its Applications) Pdf, epub, docx and torrent then this site is not for you.

only do ebook promotions online and we does not distribute any free download of ebook on this site. A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems by HanifD. Sherali Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, and Warren P.

Adams Department of Mathematical Sciences, Clemson University, Clemson, North Carolina, The motivation for this book is J J the role of tight linear/convex programming representations or relaxations in solving such discrete and continuous nonconvex programming problems. The principal thrust is to commence with a model that affords a useful representation and structure, and then to further strengthen this representation through automatic reformulation and constraint generation techniques.

a reformulation linearization technique for solving discrete and continuous nonconvex problems does ESPEC to acquire on journal-issue number and job-specific first-author-surname.

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Sherali, W.P. Adams, A reformulation-linearization technique (RLT) for solving discrete and continuous nonconvex programming problems. Math. Today, special issue on Recent Advances in Mathematical Programming, O. Gupta (ed.), XII-A, 61–78 (b) Google Scholar.

A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems 作者: Hanif D. Sherali / W. Adams 出版社: Springer 出版年: 页数: 定价: USD 装帧: Paperback ISBN: The Reformulation-Linearization-Technique (RLT) is a method that generates such tight linear pro-gramming relaxations for not only constructing exact solution algorithms, but also to design powerful heuristic procedures for large classes of discrete combinatorial and continuous nonconvex programming problems.

This book deals with the theory and applications of the Reformulation- Linearization/Convexification Technique (RL T) for solving nonconvex optimization problems. A unified treatment of discrete and continuous nonconvex programming problems is presented using this Range: $ - $ The reformulation-linearization technique (RLT) for mixed-integer programs is an automatic model enhancement approach that generates a hierarchy of relaxations spanning the spectrum from the.

The result is a cut. Moreover, this technique also works for equations with free variables. For more detail about this subject, see the book A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems by Hanif D. Sherali and W.

Adams, published in by Springer. For a discussion of mixed integer quadratic programs (MIQP) generally, see the article by. [16] H. Sherali and W. Adams, A reformulation-linearization technique for solving discrete and continuous nonconvex problems. Springer Science & Business Media,vol.

[17] A. Beck, A. Ben-Tal, and L. Tetruashvili, “A sequential parametric convex approximation method with applications to nonconvex trussFile Size: KB. Nonsmooth/Nonconvex Mechanics.-Duality Principles in Nonconvex Systems.

Sherali has written Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems with Springer as well as two other books with John Wiley & Sons.

Get this from a library! A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems. [Hanif D Sherali; Warren P Adams] -- This book addresses a new method for generating tight linear or convex programming relaxations for discrete and continuous nonconvex programming problems.

Problems of this type arise in many.A Reformulation-Linearization Technique (RLT) for semi-infinite and convex programs under mixed and general discrete restrictions.

Authors: Hanif D. Sherali: Virginia Polytechnic Institute and State University, Grado Department of Industrial and Systems Engineering (), Durham Hall, Blacksburg, VAUnited States Cited by: ISBN: OCLC Number: Description: xxiii, pages: illustrations ; 25 cm. Contents: 1. Introduction --pt.

I. Discrete Nonconvex Programs RLT Hierarchy for Mixed-Integer Zero-One Problems Generalized Hierarchy for Exploiting Special Structures in Mixed-Integer Zero-One Problems RLT Hierarchy for General Discrete Mixed-Integer Problems