Abstract: In this paper, the explicit solutions to the matrix equation AX - X̅B = C have been given by using of the so-called Leverrier algorithm. While one of the explicit solutions is stated as a ...

Abstract: The conjugate gradient (CG) solver is an important algorithm for solving the symmetric positive define systems. However, existing CG architectures on field-programmable gate arrays (FPGAs) ...

This project explores the differences between two fundamental algorithms—Preconditioned Steepest Descent (PSD) and Preconditioned Conjugate Gradient (PCG)—which are crucial for efficiently solving ...

The single-step single nucleotide polymorphism best linear unbiased prediction (ssSNPBLUP) method, such as single-step genomic BLUP (ssGBLUP), simultaneously analyses phenotypic, pedigree, and genomic ...

Copyright by Davide Palitta (University of Bologna), Martina Iannacito (University of Bologna), and Valeria Simoncini (University of Bologna). The software is ...

This is a preview. Log in through your library . Abstract In this paper, we focus on the stochastic inverse eigenvalue problem of reconstructing a stochastic matrix from the prescribed spectrum. We ...

Some algorithms based upon a projection process onto the Krylov subspace $K_m = \operatorname{Span}(r_0, Ar_0, \ldots, A^{m - 1}r_0)$ are developed, generalizing the ...

You do not have access to this resource. Krylov methods for solving linear systems are generally used with a preconditioner which accelerates the convergence. They only require matrix multiplication ...