Bounding the gap between the McCormick relaxation and the convex hull for bilinear functions
نویسندگان
چکیده
We investigate how well the graph of a bilinear function b : [0, 1]n → R can be approximated by its McCormick relaxation. In particular, we are interested in the smallest number c such that the difference between the concave upper bounding and convex lower bounding functions obtained from the McCormick relaxation approach is at most c times the difference between the concave and convex envelopes. Answering a question of Luedtke, Namazifar and Linderoth, we show that this factor c cannot be bounded by a constant independent of n. More precisely, we show that for a random bilinear function b we have asymptotically almost surely c > √ n/4. On the other hand, we prove that c 6 600 √ n, which improves the linear upper bound proved by Luedtke, Namazifar and Linderoth. In addition, we present an alternative proof for a result of Misener, Smadbeck and Floudas characterizing functions b for which the McCormick relaxation is equal to the convex hull.
منابع مشابه
Some results on the strength of relaxations of multilinear functions
We study approaches for obtaining convex relaxations of global optimization problems containing multilinear functions. Specifically, we compare the concave and convex envelopes of these functions with the relaxations that are obtained with a standard relaxation approach, due to McCormick. The standard approach reformulates the problem to contain only bilinear terms and then relaxes each term in...
متن کاملPerspective Envelopes for Bilinear Functions
The new characterization, based on perspective functions, dominates the standard McCormick convexification approach. In practice, this result is useful in the presence of linear constraints linking variables x and y, but can also be of great value in global optimization frameworks, suggesting a branching strategy based on dominance, i.e., x ď y _ x ě y. The new relaxation yields tight lower bou...
متن کاملA Global Optimization Algorithm for Nonconvex Generalized Disjunctive Programming and Applications to Process Systems
Abstract A global optimization algorithm for nonconvex Generalized Disjunctive Programming (GDP) problems is proposed in this paper. By making use of convex underestimating functions for bilinear, linear fractional and concave separable functions in the continuous variables, the convex hull of each nonlinear disjunction is constructed. The relaxed convex GDP problem is then solved in the first ...
متن کاملConvex quadratic relaxations of nonconvex quadratically constrained quadratic programs
Nonconvex quadratic constraints can be linearized to obtain relaxations in a wellunderstood manner. We propose to tighten the relaxation by using second order cone constraints, resulting in a convex quadratic relaxation. Our quadratic approximation to the bilinear term is compared to the linear McCormick bounds. The second order cone constraints are based on linear combinations of pairs of vari...
متن کاملSolving Mixed Integer Bilinear Problems Using MILP Formulations
In this paper, we examine a mixed integer linear programming (MILP) reformulation for mixed integer bilinear problems where each bilinear term involves the product of a nonnegative integer variable and a nonnegative continuous variable. This reformulation is obtained by first replacing a general integer variable with its binary expansion and then using McCormick envelopes to linearize the resul...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Math. Program.
دوره 162 شماره
صفحات -
تاریخ انتشار 2017