An R package to solve system of linear equations using (preconditioned) conjugate gradient algorithm, with improved efficiency using Armadillo templated C++ linear algebra library, and flexibility for userspecified preconditioning method.
Download cPCG_1.5.5.tar.gz file here and build from command line:
R CMD INSTALL cPCG_1.5.5.tar.gz
Get current development version from github:
devtools::install_github("styvon/cPCG")
NOTE: Mac OSX users will need to install OpenMP in order to compile the package. Check here for a solution.
Conjugate gradient method for solving system of linear equations Ax = b, where A is symmetric and positive definite, b is a column vector.
cgsolve(A, b, float tol = 1e-6, int maxIter = 1000)
When the condition number for A is large, the conjugate gradient (CG) method may fail to converge in a reasonable number of iterations. The Preconditioned Conjugate Gradient (PCG) Method applies a precondition matrix C and approaches the problem by solving C^{-1} A x = {C}^{-1} b where the symmetric and positive-definite matrix C approximates A and C^{-1}A improves the condition number of A.
pcgsolve(A, b, preconditioner = "Jacobi", float tol = 1e-6, int maxIter = 1000)
Common choices for the preconditioner include: Jacobi preconditioning, symmetric successive over-relaxation (SSOR), and incomplete Cholesky factorization.
Jacobi: The Jacobi preconditioner is the diagonal of the matrix A, with an assumption that all diagonal elements are non-zero.
SSOR: The symmetric successive over-relaxation preconditioner, implemented as M = (D+L) D^{-1} (D+L)^T.
ICC: The incomplete Cholesky factorization preconditioner.
cgsolve_sparseOMP new in v1.5.5
Sparse matrix with parallelism using OpenMP for conjugate gradient method.
# A is a sparse matrix, can be generated using 'Matrix' package function Matrix(..., sparse = TRUE)
cgsolve_sparseOMP(A, b, tol = 1e-6, maxIter = 1000, nThreads=1)
pcgsolve_sparseOMP new in v1.5.5
Sparse matrix with parallelism using OpenMP for preconditioned conjugate gradient method, Jacobi preconditioner is currently available.
# A is a sparse matrix, can be generated using 'Matrix' package function Matrix(..., sparse = TRUE)
pcgsolve_sparseOMP(A, b, preconditioner = "Jacobi", tol = 1e-6, maxIter = 1000, nThreads=1)
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