On the convergence of Han’s method for convex programming with quadratic objective

The authors present an iterative method for minimizing strictly convex quadratic functions over the intersection of a finite number of convex sets. The method consists in computing projections onto the individual sets simultaneously and the new iterate is a convex combination of those projections. The authors give convergence proofs even for the inconsistent case, i.e. when the intersection of the sets is empty.