We introduce a novel exact approach for addressing a broad spectrum of optimization problems with robust nonlinear constraints. These constraints are defined as sums of products of linear times concave (SLC) functions with respect to the uncertain parameters. Our approach synergizes a cutting set method with reformulation-perspectification techniques and branch and bound. We further extend … Read more
Robust convex constraints are difficult to handle, since finding the worst-case scenario is equivalent to maximizing a convex function. In this paper, we propose a new approach to deal with such constraints that unifies approaches known in the literature and extends them in a significant way. The extension is either obtaining better solutions than the … Read more
In this paper we survey recent work on the perspective reformulation approach that generates tight, tractable relaxations for convex mixed integer nonlinear programs (MINLP)s. This preprocessing technique is applicable to cases where the MINLP contains binary indicator variables that force continuous decision variables to take the value 0, or to belong to a convex set. … Read more