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Mixed Integer Nonlinear Programming

  • Jon Lee
  • Sven Leyffer

Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 154)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Convex MINLP

    1. Front Matter
      Pages 1-1
    2. Pierre Bonami, Mustafa Kilinç, Jeff Linderoth
      Pages 1-39
    3. Oktay Günlük, Jeff Linderoth
      Pages 61-89
  3. Disjunctive Programming

    1. Front Matter
      Pages 91-91
    2. Pietro Belotti
      Pages 117-144
  4. Nonlinear Programming

    1. Front Matter
      Pages 145-145
    2. Philip E. Gill, Elizabeth Wong
      Pages 147-224
  5. Expression Graphs

    1. Front Matter
      Pages 245-245
    2. Leo Liberti
      Pages 263-283
  6. Convexification and Linearization

    1. Front Matter
      Pages 285-285
    2. Björn Geißler, Alexander Martin, Antonio Morsi, Lars Schewe
      Pages 287-314
    3. Claudia D’Ambrosio, Jon Lee, Andreas Wächter
      Pages 315-347
    4. Andreas Lundell, Tapio Westerlund
      Pages 349-369
  7. Mixed-Integer Quadraticaly Constrained Optimization

    1. Front Matter
      Pages 371-371
    2. Samuel Burer, Anureet Saxena
      Pages 373-405
    3. Andrea Qualizza, Pietro Belotti, François Margot
      Pages 407-426
    4. Timo Berthold, Stefan Heinz, Stefan Vigerske
      Pages 427-444
  8. Combinatorial Optimization

    1. Front Matter
      Pages 445-445
    2. Jesus A. De Loera, Peter N. Malkin, Pablo A. Parrilo
      Pages 447-481
    3. Oktay Günlük, Jon Lee, Janny Leung
      Pages 513-529
  9. Complexity

    1. Front Matter
      Pages 531-531
  10. Applications

    1. Front Matter
      Pages 595-595
    2. Erica Klampfl, Yakov Fradkin
      Pages 597-629
  11. Back Matter
    Pages 671-690

About these proceedings

Introduction

​Many engineering, operations, and scientific applications include a mixture of discrete and continuous decision variables and nonlinear relationships involving the decision variables that have a pronounced effect on the set of feasible and optimal solutions. Mixed-integer nonlinear programming (MINLP) problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex functions and discrete variables. MINLP is one of the most flexible modeling paradigms available for optimization; but because its scope is so broad, in the most general cases it is hopelessly intractable. Nonetheless, an expanding body of researchers and practitioners — including chemical engineers, operations researchers, industrial engineers, mechanical engineers, economists, statisticians, computer scientists, operations managers, and mathematical programmers — are interested in solving large-scale MINLP instances.

Keywords

CONVEX MINLP DISJUNCTIVE PROGRAMMING NONLINEAR PROGRAMMING

Editors and affiliations

  • Jon Lee
    • 1
  • Sven Leyffer
    • 2
  1. 1., College of EngineeringUniversity of MichiganAnn ArborUSA
  2. 2., Mathematics and Computer ScienceArgonne National LaboratoryArgonneUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-1927-3
  • Copyright Information Springer Science+Business Media, LLC 2012
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-1926-6
  • Online ISBN 978-1-4614-1927-3
  • Series Print ISSN 0940-6573
  • Buy this book on publisher's site
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