A gradient projection method for solving split equality and split feasibility problems in Hilbert spaces

Phan Tu Vuong, Jean Jacques Strodiot, Van Hien Nguyen

    Research output: Contribution to journalArticlepeer-review

    Abstract

    In this paper, we first study in a Hilbertian framework the weak convergence of a general Gradient Projection Algorithm for minimizing a convex function of class (Formula presented.) over a convex constraint set. The way of selecting the stepsizes corresponds to the one used by López et al. for the particular case of the Split Feasibility Problem. This choice allows us to avoid the computation of operator norms. Afterwards, a relaxed version of the Gradient Projection Algorithm is considered where the feasible set is approximated by half-spaces making the projections explicit. Finally, to get the strong convergence, each step of the general Gradient Projection Method is combined with a viscosity step. This is done by adapting Halpern’s algorithm to our problem. The general scheme is then applied to the Split Equality Problem, and also to the Split Feasibility Problem.

    Original languageEnglish
    Pages (from-to)2321-2341
    Number of pages21
    JournalOptimization
    Volume64
    Issue number11
    DOIs
    Publication statusPublished - 2 Nov 2015

    Keywords

    • gradient projection method
    • relaxed algorithms
    • split equality problem
    • split feasibility problem
    • strong convergence

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