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 ...
This study addresses the challenge of efficiently and accurately computing three-dimensional undulating surface magnetic field data, which has become increasingly difficult with the development of ...
Distributed quantile regression over sensor networks via the primal–dual hybrid gradient algorithm
As one of the important statistical methods, quantile regression (QR) extends traditional regression analysis. In QR, various quantiles of the response variable are modeled as linear functions of the ...
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 ...
Abstract: For the conjugate gradient method to solve the unconstrained optimization problem, given a new interval method to obtain the direction parameters, and a new conjugate gradient algorithm is ...
Abstract: A new algorithm which combines the Quasi-Newton (QN) method and conjugate gradient (CG) method are presented in this paper. It is denoted as $\mathbf{QN}-\mathbf{WAM}+$ method and tested ...
The nonlinear conjugate gradient method is a very useful technique for solving large scale minimization problems and has wide applications in many fields. In this paper, we present a new algorithm of ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results