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arXiv · 2610.27727

Forest Characterization of When SDP-RLT Strengthens RLT for Bilinear Terms

Hayato Watanabe·Takami Sato·Kazuhide Nakata·Ken Kobayashi
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Abstract

We study two relaxations for bilinear terms in box-constrained variables: the reformulation-linearization technique (RLT) relaxation and the SDP-RLT relaxation, which combines RLT with semidefinite programming (SDP). We represent the pattern of bilinear terms by an undirected graph, with one vertex for each variable and one edge for each bilinear term. A point feasible for the RLT relaxation fixes only part of the lifted matrix of the SDP-RLT relaxation, leaving the remaining entries to be chosen subject to the diagonal RLT inequalities, and the two relaxations differ exactly when these entries cannot always be chosen so that the matrix becomes positive semidefinite. Formulating this as a positive semidefinite matrix completion problem, we show that the feasible regions of the two relaxations coincide if and only if the graph is a forest. We also measure how large the difference can be when the graph contains a complete bipartite subgraph: a bilinear objective given by a Hadamard matrix yields optimal values whose ratio grows with the size of that subgraph.

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Hayato Watanabe, Takami Sato, Kazuhide Nakata, Ken Kobayashi. 2026-10-20. Forest Characterization of When SDP-RLT Strengthens RLT for Bilinear Terms. https://arxiv.org/abs/2610.27727

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