ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...

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 ...

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

Abstract: Motivated by the success of Shanno's memoryless Conjugate Gradient (CG) methods [28,29], this paper derives a new scaled quasi-Newton like CG algorithm that utilizes an update formula that ...

ABSTRACT: In this paper, we consider a more general bi-level optimization problem, where the inner objective function is consisted of three convex functions, involving a smooth and two non-smooth ...

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 ...

Programming different types of numerical methods using MATLAB. All codes have been developed from the scratch during the academic year 2016-2017 for Numerical Analysis course at Shiraz University.

This file explores the working of various Gradient Descent Algorithms to reach a solution. Algorithms used are: Batch Gradient Descent, Mini Batch Gradient Descent, and Stochastic Gradient Descent ...