slanczos {mgcv} | R Documentation |

## Compute truncated eigen decomposition of a symmetric matrix

### Description

Uses Lanczos iteration to find the truncated eigen-decomposition of a symmetric matrix.

### Usage

```
slanczos(A,k=10,kl=-1,tol=.Machine$double.eps^.5,nt=1)
```

### Arguments

`A` |
A symmetric matrix. |

`k` |
Must be non-negative. If |

`kl` |
If |

`tol` |
tolerance to use for convergence testing of eigenvalues. Error in eigenvalues will be less
than the magnitude of the dominant eigenvalue multiplied by |

`nt` |
number of threads to use for leading order iterative multiplication of A by vector. May show no speed improvement on two processor machine. |

### Details

If `kl`

is non-negative, returns the highest `k`

and lowest `kl`

eigenvalues,
with their corresponding eigenvectors. If `kl`

is negative, returns the largest magnitude `k`

eigenvalues, with corresponding eigenvectors.

The routine implements Lanczos iteration with full re-orthogonalization as described in Demmel (1997). Lanczos
iteraction iteratively constructs a tridiagonal matrix, the eigenvalues of which converge to the eigenvalues of `A`

,
as the iteration proceeds (most extreme first). Eigenvectors can also be computed. For small `k`

and `kl`

the
approach is faster than computing the full symmetric eigendecompostion. The tridiagonal eigenproblems are handled using LAPACK.

The implementation is not optimal: in particular the inner triadiagonal problems could be handled more efficiently, and there would be some savings to be made by not always returning eigenvectors.

### Value

A list with elements `values`

(array of eigenvalues); `vectors`

(matrix with eigenvectors in its columns);
`iter`

(number of iterations required).

### Author(s)

Simon N. Wood simon.wood@r-project.org

### References

Demmel, J. (1997) Applied Numerical Linear Algebra. SIAM

### See Also

### Examples

```
require(mgcv)
## create some x's and knots...
set.seed(1);
n <- 700;A <- matrix(runif(n*n),n,n);A <- A+t(A)
## compare timings of slanczos and eigen
system.time(er <- slanczos(A,10))
system.time(um <- eigen(A,symmetric=TRUE))
## confirm values are the same...
ind <- c(1:6,(n-3):n)
range(er$values-um$values[ind]);range(abs(er$vectors)-abs(um$vectors[,ind]))
```

*mgcv*version 1.9-0 Index]