r-cran-rlinsolve - 0.3.2-1+b1 main

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. Rlinsolve provides 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.