A cut generation scheme for binary polynomial optimization problems
In this talk we present a dynamic inequality generation scheme to generate valid polynomial inequalities for binary polynomial programs. When used iteratively, this scheme improves the bounds without incurring an exponential growth in the size of the relaxation. As a result, the proposed scheme is in principle scalable to large binary polynomial programming problems. We show special cases for which the proposed scheme converges to the global optimal solution. We also present several examples illustrating the computational behavior of the scheme and provide comparisons with Lasserre’s approach and with the lift-and-project method of Balas, Ceria, and Cornuéjols.