NAG Library Function Document

nag_complex_banded_eigensystem_solve (f12auc)

Note: this function uses optional parameters to define choices in the problem specification. If you wish to use default settings for all of the optional parameters, then the option setting function nag_complex_sparse_eigensystem_option (f12arc) need not be called. If, however, you wish to reset some or all of the settings please refer to Section 11 in nag_complex_sparse_eigensystem_option (f12arc) for a detailed description of the specification of the optional parameters.

1Purpose

nag_complex_banded_eigensystem_solve (f12auc) is the main solver function in a suite of functions consisting of nag_complex_sparse_eigensystem_option (f12arc), nag_complex_banded_eigensystem_init (f12atc) and nag_complex_banded_eigensystem_solve (f12auc). It must be called following an initial call to nag_complex_banded_eigensystem_init (f12atc) and following any calls to nag_complex_sparse_eigensystem_option (f12arc).
nag_complex_banded_eigensystem_solve (f12auc) returns approximations to selected eigenvalues, and (optionally) the corresponding eigenvectors, of a standard or generalized eigenvalue problem defined by complex banded non-Hermitian matrices. The banded matrix must be stored using the LAPACK column ordered storage format for complex banded non-Hermitian (see Section 3.3.4 in the f07 Chapter Introduction).

2Specification

 #include #include
 void nag_complex_banded_eigensystem_solve (Integer kl, Integer ku, const Complex ab[], const Complex mb[], Complex sigma, Integer *nconv, Complex d[], Complex z[], Complex resid[], Complex v[], Complex comm[], Integer icomm[], NagError *fail)

3Description

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 banded, complex and non-Hermitian.
Following a call to the initialization function nag_complex_banded_eigensystem_init (f12atc), nag_complex_banded_eigensystem_solve (f12auc) returns the converged approximations to eigenvalues and (optionally) the corresponding approximate eigenvectors and/or a unitary basis for the associated approximate invariant subspace. The eigenvalues (and eigenvectors) are selected from those of a standard or generalized eigenvalue problem defined by complex banded non-Hermitian matrices. There is negligible additional computational cost to obtain eigenvectors; a unitary basis is always computed, but there is an additional storage cost if both are requested.
The banded matrices $A$ and $B$ must be stored using the LAPACK column ordered storage format for banded non-Hermitian matrices; please refer to Section 3.3.4 in the f07 Chapter Introduction for details on this storage format.
nag_complex_banded_eigensystem_solve (f12auc) is based on the banded driver functions znbdr1 to znbdr4 from the ARPACK package, which uses the Implicitly Restarted Arnoldi iteration method. The method is described in Lehoucq and Sorensen (1996) and Lehoucq (2001) while its use within the ARPACK software is described in great detail in Lehoucq et al. (1998). An evaluation of software for computing eigenvalues of sparse non-Hermitian matrices is provided in Lehoucq and Scott (1996). This suite of functions offers the same functionality as the ARPACK banded driver software for complex non-Hermitian problems, but the interface design is quite different in order to make the option setting clearer and to combine the different drivers into a general purpose function.
nag_complex_banded_eigensystem_solve (f12auc), is a general purpose function that must be called following initialization by nag_complex_banded_eigensystem_init (f12atc). nag_complex_banded_eigensystem_solve (f12auc) uses options, set either by default or explicitly by calling nag_complex_sparse_eigensystem_option (f12arc), to return the converged approximations to selected eigenvalues and (optionally):
 – the corresponding approximate eigenvectors; – a unitary basis for the associated approximate invariant subspace; – both.
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, Philidelphia

5Arguments

Note: in the following description n, nev and ncv appears. In every case they should be interpretted as the value associated with the identically named argument in a prior call to nag_complex_banded_eigensystem_init (f12atc).
1:    $\mathbf{kl}$IntegerInput
On entry: the number of subdiagonals of the matrices $A$ and $B$.
Constraint: ${\mathbf{kl}}\ge 0$.
2:    $\mathbf{ku}$IntegerInput
On entry: the number of superdiagonals of the matrices $A$ and $B$.
Constraint: ${\mathbf{ku}}\ge 0$.
3:    $\mathbf{ab}\left[\mathit{dim}\right]$const ComplexInput
Note: the dimension, dim, of the array ab must be at least ${\mathbf{n}}×\left(2×{\mathbf{kl}}+{\mathbf{ku}}+1\right)$ (see nag_complex_banded_eigensystem_init (f12atc)).
On entry: must contain the matrix $A$ in LAPACK column-ordered banded storage format for non-Hermitian matrices; that is, element ${a}_{ij}$ is stored in ${\mathbf{ab}}\left[\left(j-1\right)×\left(2×{\mathbf{kl}}+{\mathbf{ku}}+1\right)+{\mathbf{kl}}+{\mathbf{ku}}+i-j\right]$, which may be written as ${\mathbf{ab}}\left[\left(2×j-1\right)×{\mathbf{kl}}+j×{\mathbf{ku}}+i-1\right]$, for $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,j-{\mathbf{ku}}\right)\le i\le \mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(n,j+{\mathbf{kl}}\right)$ and $j=1,2,\dots ,n$, (see Section 3.3.4 in the f07 Chapter Introduction).
4:    $\mathbf{mb}\left[\mathit{dim}\right]$const ComplexInput
Note: the dimension, dim, of the array mb must be at least ${\mathbf{n}}×\left(2×{\mathbf{kl}}+{\mathbf{ku}}+1\right)$ (see nag_complex_banded_eigensystem_init (f12atc)).
On entry: must contain the matrix $B$ in LAPACK column-ordered banded storage format for non-Hermitian matrices; that is, element ${a}_{ij}$ is stored in ${\mathbf{mb}}\left[\left(j-1\right)×\left(2×{\mathbf{kl}}+{\mathbf{ku}}+1\right)+{\mathbf{kl}}+{\mathbf{ku}}+i-j\right]$, which may be written as ${\mathbf{mb}}\left[\left(2×j-1\right)×{\mathbf{kl}}+j×{\mathbf{ku}}+i-1\right]$, for $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,j-{\mathbf{ku}}\right)\le i\le \mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(n,j+{\mathbf{kl}}\right)$ and $j=1,2,\dots ,n$, (see Section 3.3.4 in the f07 Chapter Introduction).
5:    $\mathbf{sigma}$ComplexInput
On entry: if the ${\mathbf{Shifted Inverse}}$ mode (see nag_complex_sparse_eigensystem_option (f12arc)) has been selected then sigma must contain the shift used; otherwise sigma is not referenced. Section 4.2 in the f12 Chapter Introduction describes the use of shift and invert transformations.
6:    $\mathbf{nconv}$Integer *Output
On exit: the number of converged eigenvalues.
7:    $\mathbf{d}\left[\mathit{dim}\right]$ComplexOutput
Note: the dimension, dim, of the array d must be at least ${\mathbf{nev}}$ (see nag_complex_banded_eigensystem_init (f12atc)).
On exit: the first nconv locations of the array d contain the converged approximate eigenvalues.
8:    $\mathbf{z}\left[\mathit{dim}\right]$ComplexOutput
Note: the dimension, dim, of the array z must be at least ${\mathbf{n}}×{\mathbf{nev}}$ if the default option ${\mathbf{Vectors}}=\mathrm{RITZ}$ (see nag_complex_sparse_eigensystem_option (f12arc)) has been selected (see nag_complex_banded_eigensystem_init (f12atc)).
On exit: if the default option ${\mathbf{Vectors}}=\mathrm{RITZ}$ (see nag_complex_sparse_eigensystem_option (f12arc)) has been selected then z contains the final set of eigenvectors corresponding to the eigenvalues held in d, otherwise z is not referenced and may be NULL. The complex eigenvector associated with an eigenvalue ${\mathbf{d}}\left[j\right]$ is stored in the corresponding array section of z, namely ${\mathbf{z}}\left[{\mathbf{n}}×\left(\mathit{j}-1\right)+\mathit{i}-1\right]$, for $\mathit{i}=1,2,\dots ,{\mathbf{n}}$ and $\mathit{j}=1,2,\dots ,{\mathbf{nconv}}$.
9:    $\mathbf{resid}\left[\mathit{dim}\right]$ComplexInput/Output
Note: the dimension, dim, of the array resid must be at least ${\mathbf{n}}$ (see nag_complex_banded_eigensystem_init (f12atc)).
On entry: need not be set unless the option ${\mathbf{Initial Residual}}$ has been set in a prior call to nag_complex_sparse_eigensystem_option (f12arc) in which case resid must contain an initial residual vector.
On exit: contains the final residual vector. This can be used as the starting residual to improve convergence on the solution of a closely related eigenproblem. This has no relation to the error residual $Ax-\lambda x$ or $Ax-\lambda Bx$.
10:  $\mathbf{v}\left[{\mathbf{n}}×{\mathbf{ncv}}\right]$ComplexOutput
On exit: if the option ${\mathbf{Vectors}}=\mathrm{SCHUR}$ or $\mathrm{RITZ}$ (see nag_complex_sparse_eigensystem_option (f12arc)) has been set and a separate array z has been passed (i.e., z does not equal v), then the first nconv sections of v, of length $n$, will contain approximate Schur vectors that span the desired invariant subspace.
The $j$th Schur vector is stored in locations ${\mathbf{v}}\left[{\mathbf{n}}×\left(\mathit{j}-1\right)+\mathit{i}-1\right]$, for $\mathit{j}=1,2,\dots ,{\mathbf{nconv}}$ and $\mathit{i}=1,2,\dots ,n$.
11:  $\mathbf{comm}\left[60\right]$ComplexCommunication Array
12:  $\mathbf{icomm}\left[140\right]$IntegerCommunication Array
13:  $\mathbf{fail}$NagError *Input/Output
The NAG error argument (see Section 3.7 in How to Use the NAG Library and its Documentation).

6Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 2.3.1.2 in How to Use the NAG Library and its Documentation for further information.
On entry, argument $〈\mathit{\text{value}}〉$ had an illegal value.
NE_COMP_BAND_FAC
NE_COMP_BAND_SOL
NE_EIGENVALUES
The number of eigenvalues found to sufficient accuracy is zero.
NE_INITIALIZATION
Either the initialization function has not been called prior to the first call of this function or a communication array has become corrupted.
NE_INT
On entry, ${\mathbf{kl}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{kl}}\ge 0$.
On entry, ${\mathbf{ku}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{ku}}\ge 0$.
NE_INTERNAL_EIGVAL_FAIL
Error in internal call to compute eigenvalues and corresponding error bounds of the current upper Hessenberg matrix. Please contact NAG.
NE_INTERNAL_EIGVEC_FAIL
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 2.7.6 in How to Use the NAG Library and its Documentation for further information.
NE_INVALID_OPTION
On entry, ${\mathbf{Vectors}}=\text{Select}$, but this is not yet implemented.
The maximum number of iterations $\text{}\le 0$, the option ${\mathbf{Iteration Limit}}$ has been set to $〈\mathit{\text{value}}〉$.
NE_NO_ARNOLDI_FAC
Could not build an Arnoldi factorization. The size of the current Arnoldi factorization $=〈\mathit{\text{value}}〉$.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 2.7.5 in How to Use the NAG Library and its Documentation for further information.
NE_NO_SHIFTS_APPLIED
No shifts could be applied during a cycle of the implicitly restarted Arnoldi iteration.
NE_OPT_INCOMPAT
The options ${\mathbf{Generalized}}$ and ${\mathbf{Regular}}$ are incompatible.
NE_OVERFLOW
Overflow occurred during transformation of Ritz values to those of the original problem.
NE_REAL_BAND_FAC
NE_REAL_BAND_SOL
NE_SCHUR_EIG_FAIL
During calculation of a Schur form, there was a failure to compute a number of eigenvalues Please contact NAG.
NE_SCHUR_REORDER
NE_TOO_MANY_ITER
The maximum number of iterations has been reached. The maximum number of $\text{iterations}=〈\mathit{\text{value}}〉$. The number of converged eigenvalues $\text{}=〈\mathit{\text{value}}〉$.
NE_ZERO_RESID
The option ${\mathbf{Initial Residual}}$ was selected but the starting vector held in resid is zero.

7Accuracy

The relative accuracy of a Ritz value, $\lambda$, is considered acceptable if its Ritz estimate $\le {\mathbf{Tolerance}}×\left|\lambda \right|$. The default ${\mathbf{Tolerance}}$ used is the machine precision given by nag_machine_precision (X02AJC).

8Parallelism and Performance

nag_complex_banded_eigensystem_solve (f12auc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
nag_complex_banded_eigensystem_solve (f12auc) makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the x06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

None.

10Example

This example constructs the matrices $A$ and $B$ using LAPACK band storage format and solves $Ax=\lambda Bx$ in shifted inverse mode using the complex shift $\sigma$.

10.1Program Text

Program Text (f12auce.c)

10.2Program Data

Program Data (f12auce.d)

10.3Program Results

Program Results (f12auce.r)

© The Numerical Algorithms Group Ltd, Oxford, UK. 2017