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)