A convex optimization approach for minimizing the ratio of indefinite quadratic functions over an ellipsoid
We consider the nonconvex problem (RQ) of minimizing the ratio of two nonconvex quadratic functions over a possibly degenerate ellipsoid. This formulation is motivated by the so-called regularized total least squares problem (RTLS), which is a special case of the problem's class we study. We prove that under a certain mild assumption on the problem's data, problem (RQ) admits an exact semidefinite programming relaxation. We then study a simple iterative procedure which is proven to converge superlinearly to a global solution of (RQ) and show that the dependency of the number of iterations on the optimality tolerance epsilon grows as O(root ln epsilon(-1)).
regularized total least squares semidefinite programming ratio of quadratic minimization nonconvex quadratic minimization strong duality fixed point algorithms convergence analysis