A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Semidefinite approximations for quadratic programs over orthogonal matrices
2009
Journal of Global Optimization
Finding global optimum of a non-convex quadratic function is in general a very difficult task even when the feasible set is a polyhedron. We show that when the feasible set of a quadratic problem consists of orthogonal matrices from R n×k , then we can transform it into a semidefinite program in matrices of order kn which has the same optimal value. This opens new possibilities to get good lower bounds for several problems from combinatorial optimization, like the Quadratic Assignment Problem
doi:10.1007/s10898-009-9499-7
fatcat:nsjxutr56bdylcgs63wl6e755y