Lifting convex inequalities for bipartite bilinear programs
Xiaoyi Gu, Santanu S. Dey, Jean-Philippe P. Richard

TL;DR
This paper develops new convex inequalities for bipartite bilinear programs using lifting techniques, introduces a subadditive approximation framework for sequence-independent lifting, and derives strong, closed-form inequalities with practical computational benefits.
Contribution
It presents a novel subadditive approximation framework for lifting inequalities in bipartite bilinear sets, enabling sequence-independent lifting and closed-form valid inequalities.
Findings
Derived a bilinear cover inequality that is second-order cone representable.
Showed the bilinear cover inequality approximates the convex hull within a constant factor.
Developed a subadditive approximation method for efficient lifting of inequalities.
Abstract
The goal of this paper is to derive new classes of valid convex inequalities for quadratically constrained quadratic programs (QCQPs) through the technique of lifting. Our first main result shows that, for sets described by one bipartite bilinear constraint together with bounds, it is always possible to sequentially lift a seed inequality that is valid for a restriction obtained by fixing variables to their bounds, when the lifting is accomplished using affine functions of the fixed variables. In this setting, sequential lifting involves solving a non-convex nonlinear optimization problem each time a variable is lifted, just as in Mixed Integer Linear Programming. To reduce the computational burden associated with this procedure, we develop a framework based on subadditive approximations of lifting functions that permits sequence-independent lifting of seed inequalities for separable…
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Taxonomy
TopicsAdvanced Optimization Algorithms Research · Optimization and Variational Analysis · Optimization and Mathematical Programming
