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Generation of Deep Cuts Using the Fundamental Disjunctive Inequality

Chapter
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Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 181)

Abstract

Recall from Chapter I that our motivation in using disjunctive programming methods is to aid us in solving nonconvex problems of the type
$$\begin{gathered} DP: minimize (fx) \hfill \\ subject to x\varepsilon X \hfill \\ \end{gathered}$$
$$\begin{gathered} x \varepsilon U S_h \hfill \\ h\varepsilon H \hfill \\ \end{gathered}$$
where f: Rn → R is lower semicontinuous, X is a closed subset of the nonnegative orthant of Rn and each Sh, h ε H is given by Equation (1.1).

Keywords

Intuitive Appeal Nonnegative Orthant Subgradient Optimization Surrogate Constraint Rectilinear Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1980

Authors and Affiliations

  1. 1.School of Industrial Engineering and Operations ResearchVirginia Polytechnic Institute and State UniversityBlacksburgUSA
  2. 2.School of Industrial and Systems EngineeringGeorgia Institute of TechnologyAtlantaUSA

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