2020
DOI: 10.1016/j.disopt.2020.100569
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Extended formulations for convex hulls of some bilinear functions

Abstract: We consider the problem of characterizing the convex hull of the graph of a bilinear function f on the n-dimensional unit cube [0, 1] n . Extended formulations for this convex hull are obtained by taking subsets of the facets of the Boolean Quadric Polytope (BQP). Extending existing results, we propose a systematic study of properties of f that guarantee that certain classes of BQP facets are sufficient for an extended formulation. We use a modification of Zuckerberg's geometric method for proving convex hull … Show more

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Cited by 16 publications
(38 citation statements)
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“…On the one hand, these extensions allow to prove convex-hull descriptions for graphs of Boolean functions over 0/1-polytopes. On the other hand, they can be applied to bilinear functions over arbitrary polytopes, generalizing the result from [11] for bilinear functions over unit-boxes. In summary, we show that Zuckerberg's method is a valuable tool for conducting convex-hull proofs.…”
Section: Contributionmentioning
confidence: 99%
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“…On the one hand, these extensions allow to prove convex-hull descriptions for graphs of Boolean functions over 0/1-polytopes. On the other hand, they can be applied to bilinear functions over arbitrary polytopes, generalizing the result from [11] for bilinear functions over unit-boxes. In summary, we show that Zuckerberg's method is a valuable tool for conducting convex-hull proofs.…”
Section: Contributionmentioning
confidence: 99%
“…In this section, we revisit Zuckerberg's method for convex-hull proofs for combinatorial decision or optimization problems (see [1,22]). We start by briefly summarizing it, based on the condensed version of the method that was derived in [11]. Then we introduce the concept of set characterizations to significantly simplify the derivation of the set construction algorithms which form the core of Zuckerberg-type convex-hull proofs.…”
Section: Geometric Convex-hull Proofs For 0/1-polytopesmentioning
confidence: 99%
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