v1.5.5 update

v1.5.5 update: 
- pcgsolve with sparse matrix and OMP implementation now available for "Jacobi" preconditioner;
- general performance improvements and bug fixes

Author: styvon

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: cPCG
 Type: Package
 Title: Efficient and Customized Preconditioned Conjugate Gradient Method for Solving System of Linear Equations
-Version: 1.5.0
-Date: 2019-04-09
+Version: 1.5.5
+Date: 2019-04-12
 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 utilizing openMP for sparse matrix format, and user-specified preconditioning method. Please check <https://github.com/styvon/cPCG> for latest updates.

R/RcppExports.R

@@ -56,9 +56,31 @@ pcgsolve <- function(A, b, preconditioner = "Jacobi", tol = 1e-6, maxIter = 1000
 #' @param tol        Tolerance for conjugate gradient algorithm.
 #' @param maxIter    Max number of iterations for conjugate gradient algorithm.
 #' @param nThreads   Number of threads for OMP.
-#' @param nSubThreads   Number of threads for OMP nested tasks.
 #' @return      A n-vector of A_inv b
-cgsolve_sparseOMP <- function(A, b, tol = 1e-6, maxIter = 1000L, nThreads = 1L, nSubThreads = 1L) {
-    .Call(`_cPCG_cgsolve_sparseOMP`, A, b, tol, maxIter, nThreads, nSubThreads)
+cgsolve_sparseOMP <- function(A, b, tol = 1e-6, maxIter = 1000L, nThreads = 1L) {
+    .Call(`_cPCG_cgsolve_sparseOMP`, A, b, tol, maxIter, nThreads)
+}
+
+#' Preconditioned conjugate gradient method
+#'
+#' Preconditioned conjugate gradient method for solving system of linear equations Ax = b,
+#' where A is symmetric and positive definite.
+#'
+#' @title Solve for x in Ax = b using preconditioned conjugate gradient method.
+#' @param A matrix, symmetric and positive definite.
+#' @param b vector, with same dimension as number of rows of A.
+#' @param preconditioner string, method for preconditioning: \code{"Jacobi"} (default), \code{"SSOR"}, or \code{"ICC"}.
+#' @param tol numeric, threshold for convergence, default is \code{1e-6}.
+#' @param maxIter numeric, maximum iteration, default is \code{1000}.
+#' @param nThreads   Number of threads for OMP.
+#' @return A vector representing solution x.
+#' @examples
+#' \dontrun{
+#' test_A <- matrix(c(4,1,1,3), ncol = 2)
+#' test_b <- matrix(1:2, ncol = 1)
+#' pcgsolve_sparseOMP(test_A, test_b, "ICC",nThreads=2)
+#' }
+pcgsolve_sparseOMP <- function(A, b, preconditioner = "Jacobi", tol = 1e-6, maxIter = 1000L, nThreads = 1L) {
+    .Call(`_cPCG_pcgsolve_sparseOMP`, A, b, preconditioner, tol, maxIter, nThreads)
 }
 

man/cgsolve_sparseOMP.Rd

@@ -47,8 +47,8 @@ BEGIN_RCPP
 END_RCPP
 }
 // cgsolve_sparseOMP
-arma::vec cgsolve_sparseOMP(const arma::sp_mat& A, const arma::vec& b, float tol, int maxIter, int nThreads, int nSubThreads);
-RcppExport SEXP _cPCG_cgsolve_sparseOMP(SEXP ASEXP, SEXP bSEXP, SEXP tolSEXP, SEXP maxIterSEXP, SEXP nThreadsSEXP, SEXP nSubThreadsSEXP) {
+arma::vec cgsolve_sparseOMP(const arma::sp_mat& A, const arma::vec& b, float tol, int maxIter, int nThreads);
+RcppExport SEXP _cPCG_cgsolve_sparseOMP(SEXP ASEXP, SEXP bSEXP, SEXP tolSEXP, SEXP maxIterSEXP, SEXP nThreadsSEXP) {
 BEGIN_RCPP
     Rcpp::RObject rcpp_result_gen;
     Rcpp::RNGScope rcpp_rngScope_gen;
@@ -57,8 +57,23 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< float >::type tol(tolSEXP);
     Rcpp::traits::input_parameter< int >::type maxIter(maxIterSEXP);
     Rcpp::traits::input_parameter< int >::type nThreads(nThreadsSEXP);
-    Rcpp::traits::input_parameter< int >::type nSubThreads(nSubThreadsSEXP);
-    rcpp_result_gen = Rcpp::wrap(cgsolve_sparseOMP(A, b, tol, maxIter, nThreads, nSubThreads));
+    rcpp_result_gen = Rcpp::wrap(cgsolve_sparseOMP(A, b, tol, maxIter, nThreads));
+    return rcpp_result_gen;
+END_RCPP
+}
+// pcgsolve_sparseOMP
+arma::vec pcgsolve_sparseOMP(const arma::sp_mat& A, arma::vec b, std::string preconditioner, float tol, int maxIter, int nThreads);
+RcppExport SEXP _cPCG_pcgsolve_sparseOMP(SEXP ASEXP, SEXP bSEXP, SEXP preconditionerSEXP, SEXP tolSEXP, SEXP maxIterSEXP, SEXP nThreadsSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type A(ASEXP);
+    Rcpp::traits::input_parameter< arma::vec >::type b(bSEXP);
+    Rcpp::traits::input_parameter< std::string >::type preconditioner(preconditionerSEXP);
+    Rcpp::traits::input_parameter< float >::type tol(tolSEXP);
+    Rcpp::traits::input_parameter< int >::type maxIter(maxIterSEXP);
+    Rcpp::traits::input_parameter< int >::type nThreads(nThreadsSEXP);
+    rcpp_result_gen = Rcpp::wrap(pcgsolve_sparseOMP(A, b, preconditioner, tol, maxIter, nThreads));
     return rcpp_result_gen;
 END_RCPP
 }
@@ -67,7 +82,8 @@ static const R_CallMethodDef CallEntries[] = {
     {"_cPCG_icc", (DL_FUNC) &_cPCG_icc, 1},
     {"_cPCG_cgsolve", (DL_FUNC) &_cPCG_cgsolve, 4},
     {"_cPCG_pcgsolve", (DL_FUNC) &_cPCG_pcgsolve, 5},
-    {"_cPCG_cgsolve_sparseOMP", (DL_FUNC) &_cPCG_cgsolve_sparseOMP, 6},
+    {"_cPCG_cgsolve_sparseOMP", (DL_FUNC) &_cPCG_cgsolve_sparseOMP, 5},
+    {"_cPCG_pcgsolve_sparseOMP", (DL_FUNC) &_cPCG_pcgsolve_sparseOMP, 6},
     {NULL, NULL, 0}
 };