Abstract
This paper concerns algorithms allowing numerical approximations of the solution of constrained saddle point problems by a sequence of finite dimensional unconstrained optimization problems. The obtained results are presented in two papers. The first one is on the theoretical results: strong convergence in Hilbert space for strongly convex- concave objective functional is proved, and an improvement in the finite dimensional case is obtained. In a following paper [3] numerical results are presented in both finite and infinite dimensional case (with application to optimal control theory) and they show the validity of the presented algorithms.
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