Mathematical Programming

, Volume 144, Issue 1–2, pp 315–346 | Cite as

Undercover: a primal MINLP heuristic exploring a largest sub-MIP

Full Length Paper Series A

Abstract

We present Undercover, a primal heuristic for nonconvex mixed-integer nonlinear programs (MINLPs) that explores a mixed-integer linear subproblem (sub-MIP) of a given MINLP. We solve a vertex covering problem to identify a smallest set of variables to fix, a so-called cover, such that each constraint is linearized. Subsequently, these variables are fixed to values obtained from a reference point, e.g., an optimal solution of a linear relaxation. Each feasible solution of the sub-MIP corresponds to a feasible solution of the original problem. We apply domain propagation to try to avoid infeasibilities, and conflict analysis to learn additional constraints from infeasibilities that are nonetheless encountered. We present computational results on a test set of mixed-integer quadratically constrained programs (MIQCPs) and MINLPs. It turns out that the majority of these instances allows for small covers. Although general in nature, we show that the heuristic is most successful on MIQCPs. It nicely complements existing root-node heuristics in different state-of-the-art solvers and helps to significantly improve the overall performance of the MINLP solver SCIP.

Keywords

Primal heuristic Mixed-integer nonlinear programming Large neighborhood search Mixed-integer quadratically constrained programming Nonconvex optimization 

Mathematics Subject Classification (2000)

90C11 90C20 90C26 90C30 90C59 

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Copyright information

© Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2013

Authors and Affiliations

  1. 1.Department of OptimizationZuse Institute BerlinBerlinGermany

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