naginterfaces.library.sparseig.real_​symm_​init

naginterfaces.library.sparseig.real_symm_init(n, nev, ncv)

real_symm_init is a setup function in a suite of functions consisting of real_symm_init, real_symm_iter(), real_symm_proc(), real_symm_option() and real_symm_monit(). It is used to find some of the eigenvalues (and optionally the corresponding eigenvectors) of a standard or generalized eigenvalue problem defined by real symmetric matrices.

The suite of functions is suitable for the solution of large sparse, standard or generalized, symmetric eigenproblems where only a few eigenvalues from a selected range of the spectrum are required.

For full information please refer to the NAG Library document for f12fa

https://www.nag.com/numeric/nl/nagdoc_29.3/flhtml/f12/f12faf.html

Parameters

nint

The order of the matrix $A$ (and the order of the matrix $B$ for the generalized problem) that defines the eigenvalue problem.

nevint

The number of eigenvalues to be computed.

ncvint

The number of Lanczos basis vectors to use during the computation.

At present there is no a priori analysis to guide the selection of $ncv$ relative to $nev$.

However, it is recommended that $ncv\ge 2\times nev+1$.

If many problems of the same type are to be solved, you should experiment with increasing $ncv$ while keeping $nev$ fixed for a given test problem.

This will usually decrease the required number of matrix-vector operations but it also increases the work and storage required to maintain the orthogonal basis vectors.

The optimal ‘cross-over’ with respect to CPU time is problem dependent and must be determined empirically.

Returns

commdict, communication object

Communication structure.

Raises

NagValueError

(errno $1$)

On entry, $n=⟨value⟩$.

Constraint: $n>0$.

(errno $2$)

On entry, $nev=⟨value⟩$.

Constraint: $nev>0$.

(errno $3$)

On entry, $ncv=⟨value⟩$, $nev=⟨value⟩$ and $n=⟨value⟩$.

Constraint: $ncv>nev$ and $ncv\le n$.

Notes

The suite of functions is designed to calculate some of the eigenvalues, $\lambda$, (and optionally the corresponding eigenvectors, $x$) of a standard eigenvalue problem $Ax=\lambda x$, or of a generalized eigenvalue problem $Ax=\lambda Bx$ of order $n$, where $n$ is large and the coefficient matrices $A$ and $B$ are sparse, real and symmetric. The suite can also be used to find selected eigenvalues/eigenvectors of smaller scale dense, real and symmetric problems.

real_symm_init is a setup function which must be called before real_symm_iter(), the reverse communication iterative solver, and before real_symm_option(), the options setting function. real_symm_proc(), is a post-processing function that must be called following a successful final exit from real_symm_iter(), while real_symm_monit() can be used to return additional monitoring information during the computation.

This setup function initializes the communication arrays, sets (to their default values) all options that can be set by you via the option setting function real_symm_option(), and checks that the lengths of the communication arrays as passed by you are of sufficient length. For details of the options available and how to set them see Other Parameters for real_symm_option.

References

Lehoucq, R B, 2001, Implicitly restarted Arnoldi methods and subspace iteration, SIAM Journal on Matrix Analysis and Applications (23), 551–562

Lehoucq, R B and Scott, J A, 1996, An evaluation of software for computing eigenvalues of sparse nonsymmetric matrices, Preprint MCS-P547-1195, Argonne National Laboratory

Lehoucq, R B and Sorensen, D C, 1996, Deflation techniques for an implicitly restarted Arnoldi iteration, SIAM Journal on Matrix Analysis and Applications (17), 789–821

Lehoucq, R B, Sorensen, D C and Yang, C, 1998, ARPACK Users’ Guide: Solution of Large-scale Eigenvalue Problems with Implicitly Restarted Arnoldi Methods, SIAM, Philadelphia