Abstract
Stochastic approximation procedures have been extensively used in solving stochastic optimization problems. Gradient projection algorithm (GPA) plays an important role in solving constrained convex minimization problems. In this paper, strong convergence result for approximate solution of a constrained stochastic convex minimization problem in Hilbert space is proved. A generalized random iterative scheme based on skewed gradient projection algorithm and a strictly pseudo-contractive mapping is used to generate the solution.
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