Package 'cPCG'

Efficient and Customized Preconditioned Conjugate Gradient Method for Solving System of Linear Equations

Description

Solves system of linear equations using (preconditioned) conjugate gradient algorithm, with improved efficiency using Armadillo templated 'C++' linear algebra library, and flexibility for user-specified preconditioning method. Please check <https://github.com/styvon/cPCG> for latest updates.

Details

Functions in this package serve the purpose of solving for x in $Ax = b$, where A is a symmetric and positive definite matrix, b is a column vector.

To improve scalability of conjugate gradient methods for larger matrices, the Armadillo templated C++ linear algebra library is used for the implementation. The package also provides flexibility to have user-specified preconditioner options to cater for different optimization needs.

The DESCRIPTION file:

Package:	cPCG
Type:	Package
Title:	Efficient and Customized Preconditioned Conjugate Gradient Method for Solving System of Linear Equations
Version:	1.0
Date:	2018-12-30
Author:	Yongwen Zhuang
Maintainer:	Yongwen Zhuang <[email protected]>
Description:	Solves system of linear equations using (preconditioned) conjugate gradient algorithm, with improved efficiency using Armadillo templated 'C++' linear algebra library, and flexibility for user-specified preconditioning method. Please check <https://github.com/styvon/cPCG> for latest updates.
Depends:	R (>= 3.0.0)
License:	GPL (>= 2)
Imports:	Rcpp (>= 0.12.19)
LinkingTo:	Rcpp, RcppArmadillo
RoxygenNote:	6.1.1
Encoding:	UTF-8
Suggests:	knitr, rmarkdown
VignetteBuilder:	knitr
NeedsCompilation:	yes
Packaged:	2018-12-30 19:47:42 UTC; zyongwen
Repository:	CRAN
Date/Publication:	2019-01-11 17:00:10 UTC

Index of help topics:

cPCG-package            Efficient and Customized Preconditioned
                        Conjugate Gradient Method for Solving System of
                        Linear Equations
cgsolve                 Conjugate gradient method
icc                     Incomplete Cholesky Factorization
pcgsolve                Preconditioned conjugate gradient method

Further information is available in the following vignettes:

cpcg-intro	cpcg-intro (source, pdf)

Author(s)

Yongwen Zhuang

References

[1] Reeves Fletcher and Colin M Reeves. “Function minimization by conjugate gradients”. In: The computer journal 7.2 (1964), pp. 149–154.

[2] David S Kershaw. “The incomplete Cholesky—conjugate gradient method for the iter- ative solution of systems of linear equations”. In: Journal of computational physics 26.1 (1978), pp. 43–65.

[3] Yousef Saad. Iterative methods for sparse linear systems. Vol. 82. siam, 2003.

[4] David Young. “Iterative methods for solving partial difference equations of elliptic type”. In: Transactions of the American Mathematical Society 76.1 (1954), pp. 92–111.

Examples

# generate test data
  test_A <- matrix(c(4,1,1,3), ncol = 2)
  test_b <- matrix(1:2, ncol = 1)
  
  # conjugate gradient method solver
  cgsolve(test_A, test_b, 1e-6, 1000)
  
  # preconditioned conjugate gradient method solver, 
  # with incomplete Cholesky factorization as preconditioner
  pcgsolve(test_A, test_b, "ICC")

---

Conjugate gradient method

Description

Conjugate gradient method for solving system of linear equations Ax = b, where A is symmetric and positive definite, b is a column vector.

Usage

cgsolve(A, b, tol = 1e-6, maxIter = 1000)

Arguments

A	matrix, symmetric and positive definite.
b	vector, with same dimension as number of rows of A.
tol	numeric, threshold for convergence, default is 1e-6.
maxIter	numeric, maximum iteration, default is 1000.

Details

The idea of conjugate gradient method is to find a set of mutually conjugate directions for the unconstrained problem

$arg min_x f(x)$

where $f(x) = 0.5 b^T A b - bx + z$ and $z$ is a constant. The problem is equivalent to solving $Ax = b$.

This function implements an iterative procedure to reduce the number of matrix-vector multiplications [1]. The conjugate gradient method improves memory efficiency and computational complexity, especially when $A$ is relatively sparse.

Value

Returns a vector representing solution x.

Warning

Users need to check that input matrix A is symmetric and positive definite before applying the function.

References

[1] Yousef Saad. Iterative methods for sparse linear systems. Vol. 82. siam, 2003.

See Also

pcgsolve

Examples

## Not run: 
test_A <- matrix(c(4,1,1,3), ncol = 2)
test_b <- matrix(1:2, ncol = 1)
cgsolve(test_A, test_b, 1e-6, 1000)

## End(Not run)

---

Incomplete Cholesky Factorization

Description

Incomplete Cholesky factorization method to generate preconditioning matrix for conjugate gradient method.

Usage

icc(A)

Arguments

A	matrix, symmetric and positive definite.

Details

Performs incomplete Cholesky factorization on the input matrix A, the output matrix is used for preconditioning in pcgsolve() if "ICC" is specified as the preconditioner.

Value

Returns a matrix after incomplete Cholesky factorization.

Warning

Users need to check that input matrix A is symmetric and positive definite before applying the function.

See Also

pcgsolve

Examples

## Not run: 
test_A <- matrix(c(4,1,1,3), ncol = 2)
out <- icc(test_A)

## End(Not run)

---

Preconditioned conjugate gradient method

Description

Preconditioned conjugate gradient method for solving system of linear equations Ax = b, where A is symmetric and positive definite, b is a column vector.

Usage

pcgsolve(A, b, preconditioner = "Jacobi", tol = 1e-6, maxIter = 1000)

Arguments

A	matrix, symmetric and positive definite.
b	vector, with same dimension as number of rows of A.
preconditioner	string, method for preconditioning: "Jacobi" (default), "SSOR", or "ICC".
tol	numeric, threshold for convergence, default is 1e-6.
maxIter	numeric, maximum iteration, default is 1000.

Details

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 $A$.

Common choices for the preconditioner include: Jacobi preconditioning, symmetric successive over-relaxation (SSOR), and incomplete Cholesky factorization [2].

Value

Returns a vector representing solution x.

Preconditioners

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 $M = (D+L) D^{-1} (D+L)^T$. [1]

ICC: The incomplete Cholesky factorization preconditioner. [2]

Warning

Users need to check that input matrix A is symmetric and positive definite before applying the function.

References

[1] David Young. “Iterative methods for solving partial difference equations of elliptic type”. In: Transactions of the American Mathematical Society 76.1 (1954), pp. 92–111.

[2] David S Kershaw. “The incomplete Cholesky—conjugate gradient method for the iter- ative solution of systems of linear equations”. In: Journal of computational physics 26.1 (1978), pp. 43–65.

See Also

cgsolve

Examples

## Not run: 
test_A <- matrix(c(4,1,1,3), ncol = 2)
test_b <- matrix(1:2, ncol = 1)
pcgsolve(test_A, test_b, "ICC")

## End(Not run)

---