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Improving the performance of DICOPT in convex MINLP problems using a feasibility pump

David Bernal Neira (bernalde***at***cmu.edu)
Stefan Vigerske (svigerske***at***gams.com)
Francisco Trespalacios (francisco.trespalacios***at***exxonmobil.com)
Ignacio Grossmann (grossmann***at***cmu.edu)

Abstract: The solver DICOPT is based on an outer-approximation algorithm used for solving mixed- integer nonlinear programming (MINLP) problems. This algorithm is very effective for solving some types of convex MINLPs. However, there are certain problems that are dicult to solve with this algorithm. One of these problems is when the nonlinear constraints are so restrictive that the nonlinear subproblems produced by the algorithm are infeasible. This problem is addressed in this paper with a feasibility pump algorithm, which modi es the objective function in order to eciently nd feasible solutions. It has been implemented as a preprocessing algorithm for DICOPT. Computational comparisons with previous versions of DICOPT and other MINLP solvers on a set of convex MINLPs demonstrate the effectiveness of the proposed algorithm in terms of solution quality and solving time.

Keywords: feasibility pump; mixed-integer nonlinear programming; primal heuristics

Category 1: Integer Programming ((Mixed) Integer Nonlinear Programming )

Citation:

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Entry Submitted: 08/17/2017
Entry Accepted: 08/18/2017
Entry Last Modified: 09/08/2017

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