LaGO: a (heuristic) Branch and Cut algorithm for nonconvex MINLPs

Original Paper

Abstract

We present a Branch and Cut algorithm of the software package LaGO to solve nonconvex mixed-integer nonlinear programs (MINLPs). A linear outer approximation is constructed from a convex relaxation of the problem. Since we do not require an algebraic representation of the problem, reformulation techniques for the construction of the convex relaxation cannot be applied, and we are restricted to sampling techniques in case of nonquadratic nonconvex functions. The linear relaxation is further improved by mixed-integer-rounding cuts. Also box reduction techniques are applied to improve efficiency. Numerical results on medium size test problems are presented to show the efficiency of the method.

Keywords

Global optimization Branch and bound Branch and Cut Outer approximation Mixed-integer nonlinear programming 

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Lufthansa Systems BerlinBerlinGermany
  2. 2.Department of MathematicsHumboldt UniversityBerlinGermany

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