After years of abstinence, China is back. With its processor, its interconnect, and its own operating system, it takes first ...

Abstract: In this letter, a graphics processing unit (GPU)-accelerated block conjugate-gradient solver with multilevel preconditioning is presented for solving a large system of sparse equations with ...

For difficult problems, in our context, problems on highly deformed meshes, the Conjugate Gradient (CG) method may converge slowly, because the linear system is very poorly conditioned, and expensive ...

This repository provides a Python implementation of the gradient projected conjugate gradient algorithm (GPCG) presented in [1] for solving bound-constrained quadratic programs of the form ...

Abstract: The symplectic Stiefel manifold is a Riemannian manifold that is a generalization of the symplectic group. In this letter, we propose novel conjugate gradient methods on the symplectic ...

Least-squares reverse-time migration (LSRTM) can overcome the problems of low resolution and unbalanced amplitude energy of deep formation imaging in reverse-time migration (RTM); hence, it can obtain ...

The nonlinear conjugate gradient (CG) method is a good alternative to the L-BFGS optimizer. Features of nonlinear CG: I already have a working implementation of Hager and Zhang's nonlinear CG as a ...

In this paper, a modified Polak-Ribière-Polyak conjugate gradient projection method is proposed for solving large scale nonlinear convex constrained monotone equations based on the projection method ...

A Sandia National Laboratories software program now installed as an additional test for the widely observed TOP500 supercomputer challenge has become increasingly prominent. The program's full ...

In this paper, we propose a method to solve coupled problem. Our computational method is mainly based on conjugate gradient algorithm. We use finite difference method for the structure and finite ...