Efficient separation of RLT cuts for implicit and explicit bilinear terms

The reformulation–linearization technique (RLT) is a prominent approach to constructing tight linear relaxations of non-convex continuous and mixed-integer optimization problems. The goal of this paper is to extend the applicability and improve the performance of RLT for bilinear product relations. First, we present a method for detecting bilinear product relations implicitly contained in mixed-integer linear programs, which is based on analyzing linear constraints with binary variables, thus enabling the application of bilinear RLT to a new class of problems. Strategies for filtering product relations are discussed and tested. Our second contribution addresses the high computational cost of RLT cut separation, which presents one of the major difficulties in applying RLT efficiently in practice. We propose a new RLT cutting plane separation algorithm which identifies combinations of linear constraints and bound factors that are expected to yield an inequality that is violated by the current relaxation solution. This algorithm is applicable to RLT cuts generated for all types of bilinear terms, including but not limited to the detected implicit products. A detailed computational study based on independent implementations in two solvers evaluates the performance impact of the proposed methods.