The minimization of a convex quadratic function under bound constraints is a fundamental building block for more complicated optimization problems. The active-set method introduced by [M. Bergounioux, K. Ito, and K. Kunisch. Primal-Dual Strategy for Constrained Optimal Control Problems. SIAM Journal on Control and Optimization, 37:1176–1194, 1999.] and [M. Bergounioux, M. Haddou, M. Hintermüller, and K. Kunisch. A comparison of interior point methods and a Moreau-Yosida based active set strategy for constrained optimal control problems. SIAM Journal on Optimization, 11(2):495–521, 2000.] has turned out to be a powerful, fast and competitive approach for this problem. [M. Hintermüller, K. Ito, and K. Kunisch. The primal-dual active set strategy as a semi-smooth newton method. SIAM Journal on Optimization, 13(3):865–888, 2003.] provide a theoretical explanation of its efficiency by interpreting it as a semismooth Newton method. One major drawback of this method lies in the fact that it is not globally convergent. Several modifications were introduced recently, that ensure global convergence. In this paper we introduce yet another modified version of this active set method, which aims at maintaining the combinatorial flavour of the original semismooth Newton method. We prove global convergence for our modified version and show it to be competitive on a variety of difficult classes of test problems.
Technical report, Alpen-Adria Universität Kla- genfurt, Mathematics, Optimization Group, TR-AAUK-M-O-16-08-03, 2016.