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A proximal-projection method for finding zeros of set-valued operators. (English) Zbl 1208.90133
Summary: We study the convergence of an iterative algorithm for finding zeros with constraints for not necessarily monotone set-valued operators in a reflexive Banach space. This algorithm, which we call the proximal-projection method is, essentially, a fixed point procedure, and our convergence results are based on new generalizations of the Browder’s demiclosedness principle. We show how the proximal-projection method can be applied for solving ill-posed variational inequalities and convex optimization problems with data given or computable by approximations only. The convergence properties of the proximal-projection method we establish also allow us to prove that the proximal point method (with Bregman distances), whose convergence was known to occur for maximal monotone operators, still converges when the operator involved in it is monotone with sequentially weakly closed graph.
MSC:
90C25Convex programming
47J25Iterative procedures (nonlinear operator equations)
47J20Inequalities involving nonlinear operators
90C30Nonlinear programming
90C48Programming in abstract spaces
47N10Applications of operator theory in optimization, convex analysis, programming, economics