A strongly convergent method for nonsmooth convex minimization in Hilbert spaces. (English) Zbl 1232.90319
Summary: 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.
Keywords:
convex minimization; nonsmooth optimization; projected subgradient algorithm; projection method; strong convergenceReferences:
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