A strongly convergent method for nonsmooth convex minimization in Hilbert spaces
A.N. Iusem | Bello Cruz, J.Y.
Convex minimization | Projection method | Nonsmooth optimization | strong convergence | Projected subgradient algorithm.
In this paper we propose a strongly convergent variant on the projected subgradient method for constrained convex minimization problems in Hilbert spaces. The advantage of the proposed method is that it converges strongly when the problem has solutions, without additional assumptions. The method also has the following desirable property: the sequence converges to the solution of the problem which lies closest to the initial iterate.