In this study, we focus on investigating a nonsmooth convex optimization problem involving the l 1-norm under a non-negative constraint, with the goal of developing an inverse-problem solver for image ...

Abstract: In order to address unconstrained optimization problems, conjugate gradient methods are frequently employed. When considering the unconstrained optimization issue, the accelerated conjugate ...

Department of Radiology, Jinling Hospital, Affiliated Nanjing Medical University, Nanjing 210002, China Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, ...

This study introduced an efficient method for solving non-linear equations. Our approach enhances the traditional spectral conjugate gradient parameter, resulting in significant improvements in the ...

This week I interviewed Senator Amy Klobuchar, Democrat of Minnesota, about her Preventing Algorithmic Collusion Act. If you don’t know what algorithmic collusion is, it’s time to get educated, ...

Federated learning enables collaborative model training by aggregating gradients from multiple clients, thus preserving their private data. However, gradient inversion attacks can compromise this ...

Stochastic gradient descent (SGD), an important optimization method in machine learning, is widely used for parameter estimation especially in online setting where data comes in stream. While this ...

MATLAB package of iterative regularization methods and large-scale test problems. This software is described in the paper "IR Tools: A MATLAB Package of Iterative Regularization Methods and ...