arpack_defaults | R Documentation |

Interface to the ARPACK library for calculating eigenvectors of sparse matrices

arpack_defaults arpack( func, extra = NULL, sym = FALSE, options = arpack_defaults, env = parent.frame(), complex = !sym )

`func` |
The function to perform the matrix-vector multiplication. ARPACK
requires to perform these by the user. The function gets the vector |

`extra` |
Extra argument to supply to |

`sym` |
Logical scalar, whether the input matrix is symmetric. Always
supply |

`options` |
Options to ARPACK, a named list to overwrite some of the default option values. See details below. |

`env` |
The environment in which |

`complex` |
Whether to convert the eigenvectors returned by ARPACK into R
complex vectors. By default this is not done for symmetric problems (these
only have real eigenvectors/values), but only non-symmetric ones. If you
have a non-symmetric problem, but you're sure that the results will be real,
then supply |

An object of class `list`

of length 14.

ARPACK is a library for solving large scale eigenvalue problems. The
package is designed to compute a few eigenvalues and corresponding
eigenvectors of a general *n* by *n* matrix *A*. It is most
appropriate for large sparse or structured matrices *A* where structured
means that a matrix-vector product `w <- Av`

requires order *n*
rather than the usual order *n^2* floating point operations.

This function is an interface to ARPACK. igraph does not contain all ARPACK routines, only the ones dealing with symmetric and non-symmetric eigenvalue problems using double precision real numbers.

The eigenvalue calculation in ARPACK (in the simplest case) involves the
calculation of the *Av* product where *A* is the matrix we work with
and *v* is an arbitrary vector. The function supplied in the `fun`

argument is expected to perform this product. If the product can be done
efficiently, e.g. if the matrix is sparse, then `arpack`

is usually
able to calculate the eigenvalues very quickly.

The `options`

argument specifies what kind of calculation to perform.
It is a list with the following members, they correspond directly to ARPACK
parameters. On input it has the following fields:

- bmat
Character constant, possible values: ‘

`I`

’, standard eigenvalue problem,*A*x=lambda*x*; and ‘`G`

’, generalized eigenvalue problem,*A*x=lambda B*x*. Currently only ‘`I`

’ is supported.- n
Numeric scalar. The dimension of the eigenproblem. You only need to set this if you call

`arpack`

directly. (I.e. not needed for`eigen_centrality`

,`page_rank`

, etc.)- which
Specify which eigenvalues/vectors to compute, character constant with exactly two characters.

Possible values for symmetric input matrices:

- "LA"
Compute

`nev`

largest (algebraic) eigenvalues.- "SA"
Compute

`nev`

smallest (algebraic) eigenvalues.- "LM"
Compute

`nev`

largest (in magnitude) eigenvalues.- "SM"
Compute

`nev`

smallest (in magnitude) eigenvalues.- "BE"
Compute

`nev`

eigenvalues, half from each end of the spectrum. When`nev`

is odd, compute one more from the high end than from the low end.

Possible values for non-symmetric input matrices:

- "LM"
Compute

`nev`

eigenvalues of largest magnitude.- "SM"
Compute

`nev`

eigenvalues of smallest magnitude.- "LR"
Compute

`nev`

eigenvalues of largest real part.- "SR"
Compute

`nev`

eigenvalues of smallest real part.- "LI"
Compute

`nev`

eigenvalues of largest imaginary part.- "SI"
Compute

`nev`

eigenvalues of smallest imaginary part.

This parameter is sometimes overwritten by the various functions, e.g.

`page_rank`

always sets ‘`LM`

’.- nev
Numeric scalar. The number of eigenvalues to be computed.

- tol
Numeric scalar. Stopping criterion: the relative accuracy of the Ritz value is considered acceptable if its error is less than

`tol`

times its estimated value. If this is set to zero then machine precision is used.- ncv
Number of Lanczos vectors to be generated.

- ldv
Numberic scalar. It should be set to zero in the current implementation.

- ishift
Either zero or one. If zero then the shifts are provided by the user via reverse communication. If one then exact shifts with respect to the reduced tridiagonal matrix

*T*. Please always set this to one.- maxiter
Maximum number of Arnoldi update iterations allowed.

- nb
Blocksize to be used in the recurrence. Please always leave this on the default value, one.

- mode
The type of the eigenproblem to be solved. Possible values if the input matrix is symmetric:

- 1
*A*x=lambda*x*,*A*is symmetric.- 2
*A*x=lambda*M*x*,*A*is symmetric,*M*is symmetric positive definite.- 3
*K*x=lambda*M*x*,*K*is symmetric,*M*is symmetric positive semi-definite.- 4
*K*x=lambda*KG*x*,*K*is symmetric positive semi-definite,*KG*is symmetric indefinite.- 5
*A*x=lambda*M*x*,*A*is symmetric,*M*is symmetric positive semi-definite. (Cayley transformed mode.)

Please note that only

`mode==1`

was tested and other values might not work properly.Possible values if the input matrix is not symmetric:

- 1
*A*x=lambda*x*.- 2
*A*x=lambda*M*x*,*M*is symmetric positive definite.- 3
*A*x=lambda*M*x*,*M*is symmetric semi-definite.- 4
*A*x=lambda*M*x*,*M*is symmetric semi-definite.

Please note that only

`mode==1`

was tested and other values might not work properly.- start
Not used currently. Later it be used to set a starting vector.

- sigma
Not used currently.

- sigmai
Not use currently.

On output the following additional fields are added:

- info
Error flag of ARPACK. Possible values:

- 0
Normal exit.

- 1
Maximum number of iterations taken.

- 3
No shifts could be applied during a cycle of the Implicitly restarted Arnoldi iteration. One possibility is to increase the size of

`ncv`

relative to`nev`

.

ARPACK can return more error conditions than these, but they are converted to regular igraph errors.

- iter
Number of Arnoldi iterations taken.

- nconv
Number of “converged” Ritz values. This represents the number of Ritz values that satisfy the convergence critetion.

- numop
Total number of matrix-vector multiplications.

- numopb
Not used currently.

- numreo
Total number of steps of re-orthogonalization.

Please see the ARPACK documentation for additional details.

A named list with the following members:

`values` |
Numeric vector, the desired eigenvalues. |

`vectors` |
Numeric matrix, the desired
eigenvectors as columns. If |

`options` |
A named
list with the supplied |

Rich Lehoucq, Kristi Maschhoff, Danny Sorensen, Chao Yang for ARPACK, Gabor Csardi csardi.gabor@gmail.com for the R interface.

D.C. Sorensen, Implicit Application of Polynomial Filters in a
k-Step Arnoldi Method. *SIAM J. Matr. Anal. Apps.*, 13 (1992), pp
357-385.

R.B. Lehoucq, Analysis and Implementation of an Implicitly Restarted Arnoldi
Iteration. *Rice University Technical Report* TR95-13, Department of
Computational and Applied Mathematics.

B.N. Parlett & Y. Saad, Complex Shift and Invert Strategies for Real
Matrices. *Linear Algebra and its Applications*, vol 88/89, pp 575-595,
(1987).

`eigen_centrality`

, `page_rank`

,
`hub_score`

, `cluster_leading_eigen`

are some of the
functions in igraph that use ARPACK.

# Identity matrix f <- function(x, extra=NULL) x arpack(f, options=list(n=10, nev=2, ncv=4), sym=TRUE) # Graph laplacian of a star graph (undirected), n>=2 # Note that this is a linear operation f <- function(x, extra=NULL) { y <- x y[1] <- (length(x)-1)*x[1] - sum(x[-1]) for (i in 2:length(x)) { y[i] <- x[i] - x[1] } y } arpack(f, options=list(n=10, nev=1, ncv=3), sym=TRUE) # double check eigen(laplacian_matrix(make_star(10, mode="undirected"))) ## First three eigenvalues of the adjacency matrix of a graph ## We need the 'Matrix' package for this if (require(Matrix)) { set.seed(42) g <- sample_gnp(1000, 5/1000) M <- as_adj(g, sparse=TRUE) f2 <- function(x, extra=NULL) { cat("."); as.vector(M %*% x) } baev <- arpack(f2, sym=TRUE, options=list(n=vcount(g), nev=3, ncv=8, which="LM", maxiter=2000)) }

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