# R/00_eigs.R In yixuan/rARPACK: Solvers for Large Scale Eigenvalue and SVD Problems

#### Documented in eigseigs_sym

##' Find a Specified Number of Eigenvalues/vectors for Square Matrix
##'
##' @description
##' This function is a simple wrapper of the \code{\link[RSpectra]{eigs}()}
##' function in the \pkg{RSpectra} package. Also see the documentation there.
##'
##' Given an \eqn{n} by \eqn{n} matrix \eqn{A},
##' function \code{eigs()} can calculate a limited
##' number of eigenvalues and eigenvectors of \eqn{A}.
##' Users can specify the selection criteria by argument
##' \code{which}, e.g., choosing the \eqn{k} largest or smallest
##' eigenvalues and the corresponding eigenvectors.
##'
##' Currently \code{eigs()} supports matrices of the following classes:
##'
##' \tabular{ll}{
##'   \code{matrix}     \tab The most commonly used matrix type,
##'                          defined in \strong{base} package.\cr
##'   \code{dgeMatrix}  \tab General matrix, equivalent to \code{matrix},
##'                          defined in \strong{Matrix} package.\cr
##'   \code{dgCMatrix}  \tab Column oriented sparse matrix, defined in
##'                          \strong{Matrix} package.\cr
##'   \code{dgRMatrix}  \tab Row oriented sparse matrix, defined in
##'                          \strong{Matrix} package.\cr
##'   \code{dsyMatrix}  \tab Symmetrix matrix, defined in \strong{Matrix}
##'                          package.\cr
##'   \code{function}   \tab Implicitly specify the matrix through a
##'                          function that has the effect of calculating
##'                          \eqn{f(x)=Ax}{f(x) = A * x}. See section
##'                          \strong{Function Interface} for details.
##' }
##'
##' \code{eigs_sym()} assumes the matrix is symmetric,
##' and only the lower triangle (or upper triangle, which is
##' controlled by the argument \code{lower}) is used for
##' computation, which guarantees that the eigenvalues and eigenvectors are
##' real, and in some cases reduces the workload. One exception is when
##' \code{A} is a function, in which case the user is responsible for the
##' symmetry of the operator.
##'
##' \code{eigs_sym()} supports "matrix", "dgeMatrix", "dgCMatrix", "dgRMatrix"
##' and "function" typed matrices.
##'
##' @param A The matrix whose eigenvalues/vectors are to be computed.
##'          It can also be a function which receives a vector \eqn{x}
##'          and calculates \eqn{Ax}{A * x}.
##'          See section \strong{Function Interface} for details.
##' @param k Number of eigenvalues requested.
##' @param which Selection criteria. See \strong{Details} below.
##' @param sigma Shift parameter. See section \strong{Shift-And-Invert Mode}.
##' @param opts Control parameters related to the computing
##'             algorithm. See \strong{Details} below.
##' @param \dots Additional arguments such as \code{n} and \code{args} that are
##'              related to the Function Interface. See
##'              \code{\link[RSpectra]{eigs}()} in the \pkg{RSpectra} package.
##' @param lower For symmetric matrices, should the lower triangle
##'              or upper triangle be used.
##'
##' @details The \code{which} argument is a character string
##' that specifies the type of eigenvalues to be computed.
##' Possible values are:
##'
##' \tabular{ll}{
##'   "LM"  \tab  The \eqn{k} eigenvalues with largest magnitude. Here the
##'               magnitude means the Euclidean norm of complex numbers.\cr
##'   "SM"  \tab  The \eqn{k} eigenvalues with smallest magnitude.\cr
##'   "LR"  \tab  The \eqn{k} eigenvalues with largest real part.\cr
##'   "SR"  \tab  The \eqn{k} eigenvalues with smallest real part.\cr
##'   "LI"  \tab  The \eqn{k} eigenvalues with largest imaginary part.\cr
##'   "SI"  \tab  The \eqn{k} eigenvalues with smallest imaginary part.\cr
##'   "LA"  \tab  The \eqn{k} largest (algebraic) eigenvalues, considering any
##'               negative sign.\cr
##'   "SA"  \tab  The \eqn{k} smallest (algebraic) eigenvalues, considering any
##'               negative sign.\cr
##'   "BE"  \tab  Compute \eqn{k} eigenvalues, half from each end of the
##'               spectrum. When \eqn{k} is odd, compute more from the high
##'               and then from the low end.
##' }
##'
##' \code{eigs()} with matrix type "matrix", "dgeMatrix", "dgCMatrix"
##' and "dgRMatrix" can use "LM",
##' "SM", "LR", "SR", "LI" and "SI".
##'
##' \code{eigs_sym()}, and \code{eigs()} with matrix type "dsyMatrix"
##' can use "LM", "SM", "LA", "SA" and "BE".
##'
##' The \code{opts} argument is a list that can supply any of the
##' following parameters:
##'
##' \describe{
##' \item{\code{ncv}}{Number of Lanzcos basis vectors to use. More vectors
##'                   will result in faster convergence, but with greater
##'                   memory use. For general matrix, \code{ncv} must satisfy
##'                   \eqn{k+2\le ncv \le n}{k+2 <= ncv <= n}, and
##'                   for symmetric matrix, the constraint is
##'                   \eqn{k < ncv \le n}{k < ncv <= n}.
##'                   Default is \code{min(n, max(2*k+1, 20))}.}
##' \item{\code{tol}}{Precision parameter. Default is 1e-10.}
##' \item{\code{maxitr}}{Maximum number of iterations. Default is 1000.}
##' \item{\code{retvec}}{Whether to compute eigenvectors. If FALSE,
##'                      only calculate and return eigenvalues.}
##' }
##'
##' @section Shift-And-Invert Mode:
##' The \code{sigma} argument is used in the shift-and-invert mode.
##'
##' When \code{sigma} is not \code{NULL}, the selection criteria specified
##' by argument \code{which} will apply to
##'
##' \deqn{\frac{1}{\lambda-\sigma}}{1/(\lambda-\sigma)}
##'
##' where \eqn{\lambda}'s are the eigenvalues of \eqn{A}. This mode is useful
##' when user wants to find eigenvalues closest to a given number.
##' For example, if \eqn{\sigma=0}, then \code{which = "LM"} will select the
##' largest values of \eqn{1/|\lambda|}, which turns out to select
##' eigenvalues of \eqn{A} that have the smallest magnitude. The result of
##' using \code{which = "LM", sigma = 0} will be the same as
##' \code{which = "SM"}, but the former one is preferable
##' in that ARPACK is good at finding large
##' eigenvalues rather than small ones. More explanation of the
##' shift-and-invert mode can be found in the SciPy document,
##' \url{http://docs.scipy.org/doc/scipy/reference/tutorial/arpack.html}.
##'
##' @section Function Interface:
##' The matrix \eqn{A} can be specified through a function with
##' the definition
##'
##' \preformatted{function(x, args)
##' {
##'     ## should return A \%*\% x
##' }}
##'
##' which receives a vector \code{x} as an argument and returns a vector
##' of the same length. The function should have the effect of calculating
##' \eqn{Ax}{A * x}, and extra arguments can be passed in through the
##' \code{args} parameter. In \code{eigs()}, user should also provide
##' the dimension of the implicit matrix through the argument \code{n}.
##'
##' @return A list of converged eigenvalues and eigenvectors.
##' \item{values}{Computed eigenvalues.}
##' \item{vectors}{Computed eigenvectors. \code{vectors[, j]} corresponds to \code{values[j]}.}
##' \item{nconv}{Number of converged eigenvalues.}
##' \item{niter}{Number of iterations used in the computation.}
##' \item{nops}{Number of matrix operations used in the computation.}
##' @author Yixuan Qiu \url{http://statr.me}
##'
##'         Jiali Mei \email{[email protected]@gmail.com}
##'
##' @export
##' @rdname eigs
##' @keywords array
##' @examples
##' library(Matrix)
##' n = 20
##' k = 5
##'
##' ## general matrices have complex eigenvalues
##' set.seed(111)
##' A1 = matrix(rnorm(n^2), n)  ## class "matrix"
##' A2 = Matrix(A1)             ## class "dgeMatrix"
##'
##' eigs(A1, k)
##' eigs(A2, k, opts = list(retvec = FALSE))  ## eigenvalues only
##'
##' ## sparse matrices
##' A1[sample(n^2, n^2 / 2)] = 0
##' A3 = as(A1, "dgCMatrix")
##' A4 = as(A1, "dgRMatrix")
##'
##' eigs(A3, k)
##' eigs(A4, k)
##'
##' ## function interface
##' f = function(x, args)
##' {
##'     as.numeric(args %*% x)
##' }
##' eigs(f, k, n = n, args = A3)
##'
##' ## symmetric matrices have real eigenvalues
##' A5 = crossprod(A1)
##' eigs_sym(A5, k)
##'
##' ## find the smallest (in absolute value) k eigenvalues of A5
##' eigs_sym(A5, k, which = "SM")
##'
##' ## another way to do this: use the sigma argument
##' eigs_sym(A5, k, sigma = 0)
##'
##' ## The results should be the same,
##' ## but the latter method is far more stable on large matrices
eigs <- function(A, k, which = "LM", sigma = NULL, opts = list(), ...)
RSpectra::eigs(A, k, which, sigma, opts, ...)

##' @rdname eigs
##' @usage eigs_sym(A, k, which = "LM", sigma = NULL, opts = list(),
##'    lower = TRUE, ...)
##' @export
eigs_sym <- function(A, k, which = "LM", sigma = NULL, opts = list(),
lower = TRUE, ...)
RSpectra::eigs_sym(A, k, which, sigma, opts, lower, ...)

yixuan/rARPACK documentation built on May 28, 2017, 9:41 a.m.