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Strong Convergence of Projected Subgradient Methods for Nonsmooth and Nonstrictly Convex Minimization

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Abstract

In this paper, we establish a strong convergence theorem regarding a regularized variant of the projected subgradient method for nonsmooth, nonstrictly convex minimization in real Hilbert spaces. Only one projection step is needed per iteration and the involved stepsizes are controlled so that the algorithm is of practical interest. To this aim, we develop new techniques of analysis which can be adapted to many other non-Fejérian methods.

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  1. Département Scientifique Interfacultaire, GRIMAAG, Université des Antilles-Guyane, Campus de Schoelcher, 97233, Cedex, Martinique (F.W.I.), France

    Paul-Emile Maingé

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  1. Paul-Emile Maingé
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Maingé, PE. Strong Convergence of Projected Subgradient Methods for Nonsmooth and Nonstrictly Convex Minimization. Set-Valued Anal 16, 899–912 (2008). https://doi.org/10.1007/s11228-008-0102-z

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