Results 121 to 130 of about 164,297 (154)

Decreasing the computing time of approximated reliabilities of genomic estimated breeding values in the single-step genomic best linear unbiased predictor using different core sizes for the algorithm for proven and young. [PDF]

JDS Commun
Sanchez-Sierra SN, Bermann M, Vukasinovic N, Sánchez-Castro MA, Misztal I, Lourenco D.   +5 more
europepmc   +1 more source

MCOAN: multimodal contrastive representation learning for cross-omics adaptive disease regulatory network prediction. [PDF]

Bioinformatics
Long J, Liu B, Li J, Zhao S.
europepmc   +1 more source

Ten practical tips and tricks to improve the effectiveness of biological network alignment. [PDF]

PLoS Comput Biol
Agapito G, Cannataro M, Cinaglia P, Milano M.   +3 more
europepmc   +1 more source

Prior-guided factorization for reliable imputation of scRNA-seq data. [PDF]

PLoS Comput Biol
Wu Y, Xu L, Aung YW, Daoud AM.
europepmc   +1 more source

Adaptive core-enhanced latent factor model for highly accurate QoS prediction. [PDF]

Front Big Data
Ai S, Li P, Fang H, Xia Y.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Generalizations of Davidson’s Method for Computing Eigenvalues of Sparse Symmetric Matrices

SIAM Journal on Scientific and Statistical Computing, 1986
The method of \textit{E. R. Davidson} [J. Comput. Phys. 17, 87-94 (1975; Zbl 0293.65022)] for computing a few eigenpairs of large sparse symmetric matrices is analyzed as a method for using diagonal preconditioning (i.e. using an approximate inverse).
Morgan, Ronald B., Scott, David S.
openaire   +2 more sources

A hybrid method for the solution of sparse power system matrices on vector computers

38th Midwest Symposium on Circuits and Systems. Proceedings, 2002
This paper describes a methodology for solving a linear system of equations on vector computer. The methodology combines direct and inverse factors. The decomposition and implementation of the direct solution in a CRAY Y-MPZE/232, and the performance results are discussed.
A. Padilha, A.R. Basso
openaire   +1 more source

A Golub--Kahan Davidson Method for Accurately Computing a Few Singular Triplets of Large Sparse Matrices

SIAM Journal on Scientific Computing, 2019
Summary: Obtaining high accuracy singular triplets for large sparse matrices is a significant challenge, especially when searching for the smallest triplets. Due to the difficulty and size of these problems, efficient methods must function iteratively, with preconditioners, and under strict memory constraints. In this research, we present a Golub-Kahan
Goldenberg, Steven, Stathopoulos, Andreas, Romero, Eloy   +2 more
openaire   +2 more sources

Computational methods for sparse matrices

Computer Physics Communications, 1980
Abstract This paper is a survey of methods currently available for processing sparse matrices in a digital computer; specifically in the solution of linear algebraic equations and the eigenproblem.
openaire   +1 more source

A New Method for Computing $��$-functions and Their Condition Numbers of Large Sparse Matrices

2016
We propose a new method for computing the $ $-functions of large sparse matrices with low rank or fast decaying singular values. The key is to reduce the computation of $ _{\ell}$-functions of a large matrix to $ _{\ell+1}$-functions of some $r$-by-$r$ matrices, where $r$ is the numerical rank of the large matrix in question.
Wu, Gang, Zhang, Lu
openaire   +1 more source