Solving Composite Fixed Point Problems with Block Updates

Abstract

Various strategies are available to construct iteratively a common fixed point of nonexpansive operators by activating only a block of operators at each iteration. In the more challenging class of composite fixed point problems involving operators that do not share common fixed points, current methods require the activation of all the operators at each iteration, and the question of maintaining convergence while updating only blocks of operators is open. We propose a method that achieves this goal and analyze its asymptotic behavior. Weak, strong, and linear convergence results are established by exploiting a connection with the theory of concentrating arrays. Applications to several nonlinear and nonsmooth analysis problems are presented, ranging from monotone inclusions and inconsistent feasibility problems, to variational inequalities and minimization problems arising in data science.

Keywords

• averaged operator
• constrained minimization
• forward-backward splitting
• fixed point iterations
• monotone operator
• nonexpansive operator
• variational inequality
• 47j26
• 47n10
• 90c25
• 47h05