Sciweavers

Share
IPCO
2008

A Comparative Study of Linear and Semidefinite Branch-and-Cut Methods for Solving the Minimum Graph Bisection Problem

8 years 3 months ago
A Comparative Study of Linear and Semidefinite Branch-and-Cut Methods for Solving the Minimum Graph Bisection Problem
Abstract. Semidefinite relaxations are known to deliver good approximations for combinatorial optimization problems like graph bisection. Using the spectral bundle method it is possible to exploit structural properties of the underlying problem and to apply, even to sparse large scale instances, cutting plane methods, probably the most successful technique in linear programming. We set up a common branch-and-cut framework for linear and semidefinite relaxations of the minimum graph bisection problem. It incorporates separation algorithms for valid inequalities presented in the recent study [?] of the facial structure of the associated polytope. Extensive numerical experiments show that the semidefinite branch-and-cut approach outperforms the classical simplex approach on a clear majority of the sparse large scale test instances. On instances from compiler design the simplex approach is faster.
Michael Armbruster, Marzena Fügenschuh, Chris
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where IPCO
Authors Michael Armbruster, Marzena Fügenschuh, Christoph Helmberg, Alexander Martin
Comments (0)
books