Learning to Accelerate Partitioning Algorithms for the Global Optimization of Nonconvex Quadratically-Constrained Quadratic Programs

We learn optimal instance-specific heuristics to accelerate partitioning algorithms for solving nonconvex quadratically-constrained quadratic programs (QCQPs) to global optimality. Specifically, we propose the novel problem of strong partitioning to optimally partition the domains of variables participating in nonconvex terms within a QCQP without sacrificing guarantees of global optimality. We then design a local optimization method … Read more