Strengthened Semidefinite Relaxations via a Second Lifting for the Max-Cut Problem
Miguel F. Anjos (manjosmath.uwaterloo.ca)
Abstract: In this paper we study two strengthened semidefinite programming relaxations for the Max-Cut problem. Our results hold for every instance of Max-Cut; in particular, we make no assumptions about the edge weights. We prove that the first relaxation provides a strengthening of the Goemans-Williamson relaxation. The second relaxation is a further tightening of the first one and we prove that its feasible set corresponds to a convex set that is larger than the cut polytope but nonetheless is strictly contained in the intersection of the elliptope and the metric polytope. Both relaxations are obtained using Lagrangian relaxation. Hence our results also exemplify the strength and flexibility of Lagrangian relaxation for obtaining a variety of SDP relaxations with different properties. We also address some practical issues in the solution of these SDP relaxations. Because Slater's constraint qualification fails for both of them, we project their feasible sets onto a lower dimensional space in a way that does not affect the sparsity of these relaxations but guarantees Slater's condition. Some preliminary numerical results are included.
Keywords: Max-cut problem, semidefinite programming relaxations, Lagrangian relaxation, cut polytope, metric polytope.
Category 1: Linear, Cone and Semidefinite Programming (Semi-definite Programming )
Category 2: Combinatorial Optimization
Citation: Discrete Applied Mathematics, Vol. 119 (1-2) (2002) pp. 79-106.
Entry Submitted: 02/09/2001
Modify/Update this entry
|Visitors||Authors||More about us||Links|
Search, Browse the Repository
Give us feedback
|Optimization Journals, Sites, Societies|