Approximating global quadratic optimization
with convex quadratic constraints
We consider the problem of approximating the global maximum of a
quadratic program (QP) subject to convex non-homogeneous quadratic
constraints. We prove an approximation quality bound that is related
to a condition number of the convex feasible set; and is the currently
best for approximating certain problems, such as quadratic
optimization over the assignment polytope.
Working Paper, Department of Management Sciences,
The University of Iowa, Iowa City, Iowa 52242, U.S.A.