Rounding-based heuristics for nonconvex MINLPs
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12532_2011_Article_32.pdf
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Author(s)
Nannicini, Giacomo
Belotti, Pietro
Date Issued
September 2011
Journal
Mathematical Programming Computation
Publisher
Springer Berlin Heidelberg
Citation
Nannicini, Giacomo, and Pietro Belotti. “Rounding-Based Heuristics for Nonconvex MINLPs.” Mathematical Programming Computation 4.1 (2012): 1–31.
Version
Author's final manuscript
Abstract
We propose two primal heuristics for nonconvex mixed-integer nonlinear programs. Both are based on the idea of rounding the solution of a continuous nonlinear program subject to linear constraints. Each rounding step is accomplished through the solution of a mixed-integer linear program. Our heuristics use the same algorithmic scheme, but they differ in the choice of the point to be rounded (which is feasible for nonlinear constraints but possibly fractional) and in the linear constraints. We propose a feasibility heuristic, that aims at finding an initial feasible solution, and an improvement heuristic, whose purpose is to search for an improved solution within the neighborhood of a given point. The neighborhood is defined through local branching cuts or box constraints. Computational results show the effectiveness in practice of these simple ideas, implemented within an open-source solver for nonconvex mixed-integer nonlinear programs.
MIT Department
Sloan School of Management
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s12532-011-0032-x
