Rlinsolve: Iterative Solvers for (Sparse) Linear System of Equations

Solving a system of linear equations is one of the most fundamental computational problems for many fields of mathematical studies, such as regression problems from statistics or numerical partial differential equations. We provide basic stationary iterative solvers such as Jacobi, Gauss-Seidel, Successive Over-Relaxation and SSOR methods. Nonstationary, also known as Krylov subspace methods are also provided. Sparse matrix computation is also supported in that solving large and sparse linear systems can be manageable using 'Matrix' package along with 'RcppArmadillo'. For a more detailed description, see a book by Saad (2003) <doi:10.1137/1.9780898718003>.

Version: 0.1.1
Depends: R (≥ 3.3.0), bigmemory
Imports: Rcpp (≥ 0.12.4), Matrix
LinkingTo: bigmemory, BH, Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, microbenchmark, optR, pcg
Published: 2017-10-08
Author: Kisung You [aut, cre]
Maintainer: Kisung You <kyou at nd.edu>
License: GPL (≥ 3)
NeedsCompilation: yes
CRAN checks: Rlinsolve results


Reference manual: Rlinsolve.pdf
Vignettes: Rlinsolve_basics
Package source: Rlinsolve_0.1.1.tar.gz
Windows binaries: r-devel: Rlinsolve_0.1.1.zip, r-release: Rlinsolve_0.1.1.zip, r-oldrel: Rlinsolve_0.1.1.zip
OS X El Capitan binaries: r-release: Rlinsolve_0.1.1.tgz
OS X Mavericks binaries: r-oldrel: Rlinsolve_0.1.1.tgz
Old sources: Rlinsolve archive

Reverse dependencies:

Reverse imports: matdist


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