For , where is convex with an -Lipschitz gradient and is convex, use a proximal -step for the full section and an explicit -step for . Monotonicity of the two selected subgradients of combines with cocoercivity of to make the full update firmly nonexpansive for . Existence of a minimizer then gives convergence, even though the full objective is not smooth.
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