6575
2017
2017
eng
499
517
3
33
article
Taylor & Francis
0
2017-11-03
2017-10-10
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From feasibility to improvement to proof: three phases of solving mixed-integer programs
Modern mixed-integer programming (MIP) solvers employ dozens of auxiliary algorithmic components to support the branch-and-bound search in finding and improving primal solutions and in strengthening the dual bound. Typically, all components are tuned to minimize the average running time to prove optimality. In this article, we take a different look at the run of a MIP solver. We argue that the solution process consists of three distinct phases, namely achieving feasibility, improving the incumbent solution, and proving optimality. We first show that the entire solving process can be improved by adapting the search strategy with respect to the phase-specific aims using different control tunings. Afterwards, we provide criteria to predict the transition between the individual phases and evaluate the performance impact of altering the algorithmic behaviour of the non-commercial MIP solver Scip at the predicted phase transition points.
Optimization Methods and Software
10.1080/10556788.2017.1392519
yes
urn:nbn:de:0297-zib-61607
Timo Berthold
Regine Kossick
Gregor Hendel
Thorsten Koch
Mathematical Optimization
Mathematical Optimization Methods
Berthold, Timo
Hendel, Gregor
Koch, Thorsten
ASTfSCM
MIP-ZIBOPT
MODAL-SynLab
Siemens
MODAL-Gesamt