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, ...
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 ...
Efficient Shift-and-Invert Preconditioning for Multi-GPU Accelerated Density Functional Calculations
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. No ...
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 ...
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: To solve the problem of large steady state residual error of momentum constant modulus algorithm (CMA) blind equalization, a momentum CMA blind equalization ...
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