This paper studies a novel algorithm for nonconvex composite minimization which can be
interpreted in terms of dual space nonlinear preconditioning for the classical proximal …

We systematically study the local single-valuedness of the Bregman proximal mapping and
local smoothness of the Bregman–Moreau envelope of a nonconvex function under relative …

We consider the differentiation of the value function for parametric optimization problems.
Such problems are ubiquitous in machine learning applications such as structured support …

This article introduces a novel model that restores color images degraded by blurring and
mixed random-valued (RV) impulse and Gaussian noise. The model includes a data-fidelity …

In this work we consider numerical efficiency and convergence rates for solvers of non-
convex multi-penalty formulations when reconstructing sparse signals from noisy linear …

This thesis considers two complementary approaches for decoupling in composite
optimization problems by lower relaxations. The first approach is based on component-wise …