RSpectra is the R interface of Spectra.
It provides functions eigs()
and eigs_sym()
for eigenvalue problems,
and svds()
for truncated (partial) SVD. These functions are generic, meaning
that different matrix types in R, including sparse matrices, are supported.
Below is a list of implemented ones:
matrix
(defined in base R)dgeMatrix
(defined in Matrix package, for general matrices)dsyMatrix
(defined in Matrix package, for symmetric matrices)dgCMatrix
(defined in Matrix package, for column oriented sparse matrices)dgRMatrix
(defined in Matrix package, for row oriented sparse matrices)function
(implicitly specify the matrix by providing a function that calculates matrix product A %*% x
)We first generate some matrices:
library(RSpectra)
library(Matrix)
n = 20
k = 5
set.seed(111)
A1 = matrix(rnorm(n^2), n) ## class "matrix"
A2 = Matrix(A1) ## class "dgeMatrix"
General matrices have complex eigenvalues:
eigs(A1, k)
eigs(A2, k, opts = list(retvec = FALSE)) ## eigenvalues only
RSpectra also works on 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 is also supported:
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)
To find the smallest (in absolute value) k
eigenvalues of A5
,
we have two approaches:
eigs_sym(A5, k, which = "SM")
eigs_sym(A5, k, sigma = 0)
The results should be the same, but the latter method is preferred, since it is much more stable on large matrices.
For SVD problems, users can can specify the number of singular values
(k
), number of left singular vectors (nu
) and number of right
singular vectors(nv
).
m = 100
n = 20
k = 5
set.seed(111)
A = matrix(rnorm(m * n), m)
svds(A, k)
svds(t(A), k, nu = 0, nv = 3)
Similar to eigs()
, svds()
supports sparse matrices:
A[sample(m * n, m * n / 2)] = 0
Asp1 = as(A, "dgCMatrix")
Asp2 = as(A, "dgRMatrix")
svds(Asp1, k)
svds(Asp2, k, nu = 0, nv = 0)
The function-by-function reference can be found in
this manual
and in the built-in help system of R by typing ?RSpectra::eigs
and
?RSpectra::svds