Adaptive proximal gradient methods are universal without approximation
We show that adaptive proximal gradient methods for convex problems are not restricted to
traditional Lipschitzian assumptions. Our analysis reveals that a class of linesearch-free …
traditional Lipschitzian assumptions. Our analysis reveals that a class of linesearch-free …
Power proximal point and augmented Lagrangian method
In this paper we study an unconventional inexact Augmented Lagrangian Method (ALM) for
convex optimization problems, wherein the penalty term is a potentially non-Euclidean norm …
convex optimization problems, wherein the penalty term is a potentially non-Euclidean norm …
Anisotropic proximal point algorithm
E Laude, P Patrinos - arXiv preprint arXiv:2312.09834, 2023 - arxiv.org
In this paper we study a nonlinear dual space preconditioning approach for the relaxed
Proximal Point Algorithm (PPA) with application to monotone inclusions, called anisotropic …
Proximal Point Algorithm (PPA) with application to monotone inclusions, called anisotropic …
Dualities for non-Euclidean smoothness and strong convexity under the light of generalized conjugacy
Relative smoothness and strong convexity have recently gained considerable attention in
optimization. These notions are generalizations of the classical Euclidean notions of …
optimization. These notions are generalizations of the classical Euclidean notions of …