2010
DOI: 10.1007/s10107-010-0371-9
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Convex relaxations of non-convex mixed integer quadratically constrained programs: extended formulations

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Cited by 82 publications
(72 citation statements)
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“…In addition to the cut generators described in the previous sections, we also used the Cut Generating Linear Programming (CGLP) framework described in our companion paper [17] (also see [2]) to derive disjunctive cuts. Recall that the CGLP framework requires a polyhedral relaxation of MIQCP and a class of disjunctions that is satisfied by every feasible solution to the problem.…”
Section: Computational Resultsmentioning
confidence: 99%
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“…In addition to the cut generators described in the previous sections, we also used the Cut Generating Linear Programming (CGLP) framework described in our companion paper [17] (also see [2]) to derive disjunctive cuts. Recall that the CGLP framework requires a polyhedral relaxation of MIQCP and a class of disjunctions that is satisfied by every feasible solution to the problem.…”
Section: Computational Resultsmentioning
confidence: 99%
“…Our computational results have two highlights. First, on the GLOBALLib instances we are able to generate relaxations that are almost as strong as those proposed in [17] even though our computing times are about 100 times smaller, on average. Second, on the box QP instances the strengthened relaxations generated by our code are almost as strong as the MIQCP-SDP relaxation and can be solved in less than 2 sec even for larger instances with 100 variables; the MIQCP-SDP relaxations of the same set of instances can take up to a couple of hours to solve using a state-of-the-art SDP solver.…”
Section: Introductionmentioning
confidence: 87%
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