This paper studies first order methods for solving smooth minimax optimization problems
$\min_x\max_y g (x, y) $ where $ g (\cdot,\cdot) $ is smooth and $ g (x,\cdot) $ is concave for …

This paper deals with distributed reinforcement learning problems with safety constraints. In
particular, we consider that a team of agents cooperate in a shared environment, where …

We propose a practical inexact augmented Lagrangian method (iALM) for nonconvex
problems with nonlinear constraints. We characterize the total computational complexity of …

We consider minimizing a nonconvex, smooth function $ f $ on a Riemannian manifold
$\mathcal {M} $. We show that a perturbed version of the gradient descent algorithm …

In this paper, an inexact proximal-point penalty method is studied for constrained
optimization problems, where the objective function is non-convex, and the constraint …

The augmented Lagrangian method (ALM) is one of the most useful methods for constrained
optimization. Its convergence has been well established under convexity assumptions or …

This paper proposes two efficient algorithms for computing approximate second-order
stationary points (SOSPs) of problems with generic smooth non-convex objective functions …

We describe an algorithm based on a logarithmic barrier function, Newton's method and
linear conjugate gradients that seeks an approximate minimizer of a smooth function over …

The standard simplex in, also known as the probability simplex, is the set of nonnegative
vectors whose entries sum up to 1. It frequently appears as a constraint in optimization …

Nonsmooth optimization problems arising in practice, whether in signal processing,
statistical estimation, or modern machine learning, tend to exhibit beneficial smooth …