The backtracking line-search is an effective technique to automatically tune the step-size in
smooth optimization. It guarantees similar performance to using the theoretically optimal …

Many algorithms for high-dimensional regression problems require the calibration of
regularization hyperparameters. This, in turn, often requires the knowledge of the unknown …

In large-scale time series forecasting, one often encounters the situation where the temporal
patterns of time series, while drifting over time, differ from one another in the same dataset …

In plug-and-play (PnP) regularization, the knowledge of the forward model is combined with
a powerful denoiser to obtain state-of-the-art image reconstructions. This is typically done by …

In this paper, we consider the composite optimization problems over the Stiefel manifold. A
successful method to solve this class of problems is the proximal gradient method proposed …

Abstract Learning to Optimize (L2O), a technique that utilizes machine learning to learn an
optimization algorithm automatically from data, has gained arising attention in recent years …

We study the use of inverse harmonic Rayleigh quotients with target for the stepsize
selection in gradient methods for nonlinear unconstrained optimization problems. This not …

This paper proposes new proximal Newton-type methods with a diagonal metric for solving
composite optimization problems whose objective function is the sum of a twice continuously …

In this paper we propose a proximal Gauss-Newton method for the penalized nonlinear least
squares optimization problem arising from regularization of ill-posed nonlinear inverse …

This paper introduces a novel algorithm, the Perturbed Proximal Preconditioned SPIDER
algorithm (3P-SPIDER), designed to solve finite sum non-convex composite optimization. It …