rARPACKhas been superseded by RSpectra to avoid the confusion on package name.

rARPACKwas originally an R wrapper of the ARPACK library to solve large scale eigenvalue/vector problems. From version 0.8-0, it changed the backend to the Spectra library, so theoretically it no longer depended on ARPACK since then. From version 0.11-0,rARPACKwas simply a shell of theRSpectrapackage.

Old sources are kept in the archive branch.

New users of

rARPACKare advised to switch to theRSpectrapackage.

**rARPACK** is typically used to compute a few eigen
values/vectors of an `n`

by `n`

matrix, e.g., the
`k`

largest eigen values, which is usually more efficient
than `eigen()`

if `k << n`

.

Currently this package provides function `eigs()`

for
eigenvalue/eigenvector problems, and `svds()`

for truncated
SVD. 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(Matrix)
= 20
n = 5
k
set.seed(111)
= matrix(rnorm(n^2), n) ## class "matrix"
A1 = Matrix(A1) ## class "dgeMatrix" A2
```

General matrices have complex eigenvalues:

```
eigs(A1, k)
eigs(A2, k, opts = list(retvec = FALSE)) ## eigenvalues only
```

**rARPACK** also works on sparse matrices:

```
sample(n^2, n^2 / 2)] = 0
A1[= as(A1, "dgCMatrix")
A3 = as(A1, "dgRMatrix")
A4
eigs(A3, k)
eigs(A4, k)
```

Function interface is also supported:

```
= function(x, args)
f
{as.numeric(args %*% x)
}eigs(f, k, n = n, args = A3)
```

Symmetric matrices have real eigenvalues.

```
= crossprod(A1)
A5 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 far more stable on large matrices.

For SVD problems, you can specify the number of singular values
(`k`

), number of left singular vectors (`nu`

) and
number of right singular vectors(`nv`

).

```
= 100
m = 20
n = 5
k set.seed(111)
= matrix(rnorm(m * n), m)
A
svds(A, k)
svds(t(A), k, nu = 0, nv = 3)
```

Similar to `eigs()`

, `svds()`

supports sparse
matrices:

```
sample(m * n, m * n / 2)] = 0
A[= as(A, "dgCMatrix")
Asp1 = as(A, "dgRMatrix")
Asp2
svds(Asp1, k)
svds(Asp2, k, nu = 0, nv = 0)
```