Variable Smoothing for Convex Optimization Problems Using Stochastic Gradients
Variable Smoothing for Convex Optimization Problems Using Stochastic Gradients
We study minimization of a structured objective function, being the sum of a smooth function and a composition of a weakly convex function with a linear operator. Applications include image reconstruction problems with regularizers that introduce less bias than the standard convex regularizers. We develop a variable smoothing algorithm, based …