We consider a composite optimization problem where the sum of a continuously
differentiable and a merely lower semicontinuous function has to be minimized. The …

Composite optimization problems, where the sum of a smooth and a merely lower
semicontinuous function has to be minimized, are often tackled numerically by means of …

There is growing interest in nonconvex minimax problems that is driven by an abundance of
applications. Our focus is on nonsmooth, nonconvex-strongly concave minimax, thus …

In this paper, we consider the minimization of a nonsmooth nonconvex objective function $ f
(x) $ over a closed convex subset $\mathcal {X} $ of $\mathbb {R}^ n $, with additional …

In this paper, we consider constrained optimization problems with convex objective and
smooth convex functional constraints. We propose a new stochastic gradient algorithm …

Nonlinearly constrained nonconvex and nonsmooth optimization models play an
increasingly important role in machine learning, statistics, and data analytics. In this paper …

This paper proposes a new approach for solving a structured nonsmooth nonconvex
optimization problem with nonlinear equality constraints, where both the objective function …

In this paper, we propose an inertial alternating direction method of multipliers for solving a
class of non-convex multi-block optimization problems with nonlinear coupling constraints …

This work introduces an unconventional inexact augmented Lagrangian method, where the
augmenting term is a Euclidean norm raised to a power between one and two. The …

In this paper we consider a nonconvex optimization problem with nonlinear equality
constraints. We assume that both, the objective function and the functional constraints, are …