f12 Chapter Contents
f12 Chapter Introduction
NAG C Library Manual

# NAG Library Function Documentnag_real_banded_sparse_eigensystem_sol (f12agc)

Note: this function uses optional arguments to define choices in the problem specification. If you wish to use default settings for all of the optional arguments, then the option setting routine nag_real_sparse_eigensystem_option (f12adc) need not be called. If, however, you wish to reset some or all of the settings please refer to Section 10 in nag_real_sparse_eigensystem_option (f12adc) for a detailed description of the specification of the optional arguments.

## 1  Purpose

nag_real_banded_sparse_eigensystem_sol (f12agc) is the main solver function in a suite of functions consisting of nag_real_sparse_eigensystem_option (f12adc), nag_real_banded_sparse_eigensystem_init (f12afc) and nag_real_banded_sparse_eigensystem_sol (f12agc), that must be called following an initial call to nag_real_banded_sparse_eigensystem_init (f12afc) and following any calls to nag_real_sparse_eigensystem_option (f12adc).
nag_real_banded_sparse_eigensystem_sol (f12agc) returns approximations to selected eigenvalues, and (optionally) the corresponding eigenvectors, of a standard or generalized eigenvalue problem defined by real banded nonsymmetric matrices. The banded matrix must be stored using the LAPACK column ordered storage format for real banded nonsymmetric (see Section 3.3.2 in the f07 Chapter Introduction).

## 2  Specification

 #include #include
 void nag_real_banded_sparse_eigensystem_sol (Integer kl, Integer ku, const double ab[], const double mb[], double sigmar, double sigmai, Integer *nconv, double dr[], double di[], double z[], double resid[], double v[], double comm[], Integer icomm[], NagError *fail)

## 3  Description

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, real and nonsymmetric.
Following a call to the initialization function nag_real_banded_sparse_eigensystem_init (f12afc), nag_real_banded_sparse_eigensystem_sol (f12agc) returns the converged approximations to eigenvalues and (optionally) the corresponding approximate eigenvectors and/or an orthonormal basis for the associated approximate invariant subspace. The eigenvalues (and eigenvectors) are selected from those of a standard or generalized eigenvalue problem defined by real banded nonsymmetric matrices. There is negligible additional computational cost to obtain eigenvectors; an orthonormal 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 nonsymmetric matrices; please refer to Section 3.3.2 in the f07 Chapter Introduction for details on this storage format.
nag_real_banded_sparse_eigensystem_sol (f12agc) is based on the banded driver functions dnbdr1 to dnbdr6 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 nonsymmetric matrices is provided in Lehoucq and Scott (1996). This suite of functions offers the same functionality as the ARPACK banded driver software for real nonsymmetric 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_real_banded_sparse_eigensystem_sol (f12agc), is a general purpose forward communication function that must be called following initialization by nag_real_banded_sparse_eigensystem_init (f12afc). nag_real_banded_sparse_eigensystem_sol (f12agc) uses options, set either by default or explicitly by calling nag_real_sparse_eigensystem_option (f12adc), to return the converged approximations to selected eigenvalues and (optionally):
 – the corresponding approximate eigenvectors; – an orthonormal basis for the associated approximate invariant subspace; – both.

## 4  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, Philidelphia

## 5  Arguments

1:     klIntegerInput
On entry: the number of subdiagonals of the matrices $A$ and $B$.
Constraint: ${\mathbf{kl}}\ge 0$.
2:     kuIntegerInput
On entry: the number of superdiagonals of the matrices $A$ and $B$.
Constraint: ${\mathbf{ku}}\ge 0$.
3:     ab[$\mathit{dim}$]const doubleInput
Note: the dimension, dim, of the array ab must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}×\left(2×{\mathbf{kl}}+{\mathbf{ku}}+1\right)\right)$ (see nag_real_banded_sparse_eigensystem_init (f12afc)).
On entry: must contain the matrix $A$ in LAPACK column-ordered banded storage format for nonsymmetric matrices (see Section 3.3.2 in the f07 Chapter Introduction).
4:     mb[$\mathit{dim}$]const doubleInput
Note: the dimension, dim, of the array mb must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}×\left(2×{\mathbf{kl}}+{\mathbf{ku}}+1\right)\right)$ (see nag_real_banded_sparse_eigensystem_init (f12afc)).
On entry: must contain the matrix $B$ in LAPACK column-ordered banded storage format for nonsymmetric matrices (see Section 3.3.2 in the f07 Chapter Introduction).
5:     sigmardoubleInput
On entry: if one of the ${\mathbf{Shifted Inverse Real}}$ modes have been selected then sigmar contains the real part of the shift used; otherwise sigmar is not referenced.
6:     sigmaidoubleInput
On entry: if one of the ${\mathbf{Shifted Inverse Real}}$ modes have been selected then sigmai contains the imaginary part of the shift used; otherwise sigmai is not referenced.
7:     nconvInteger *Output
On exit: the number of converged eigenvalues.
8:     dr[$\mathit{dim}$]doubleOutput
Note: the dimension, dim, of the array dr must be at least ${\mathbf{nev}}+1$ (see nag_real_banded_sparse_eigensystem_init (f12afc)).
On exit: the first nconv locations of the array dr contain the real parts of the converged approximate eigenvalues. The number of eigenvalues returned may be one more than the number requested by nev since complex values occur as conjugate pairs and the second in the pair can be returned in position ${\mathbf{nev}}+1$ of the array.
9:     di[$\mathit{dim}$]doubleOutput
Note: the dimension, dim, of the array di must be at least ${\mathbf{nev}}+1$ (see nag_real_banded_sparse_eigensystem_init (f12afc)).
On exit: the first nconv locations of the array di contain the imaginary parts of the converged approximate eigenvalues. The number of eigenvalues returned may be one more than the number requested by nev since complex values occur as conjugate pairs and the second in the pair can be returned in position ${\mathbf{nev}}+1$ of the array.
10:   z[$\mathit{dim}$]doubleOutput
Note: the dimension, dim, of the array z must be at least ${\mathbf{n}}×\left({\mathbf{nev}}+1\right)$ (see nag_real_banded_sparse_eigensystem_init (f12afc)).
On exit: if the default option ${\mathbf{Vectors}}=\text{Ritz}$ has been selected then z contains the final set of eigenvectors corresponding to the eigenvalues held in dr and di. The complex eigenvector associated with the eigenvalue with positive imaginary part is stored in two consecutive array segments. The first segment holds the real part of the eigenvector and the second holds the imaginary part. The eigenvector associated with the eigenvalue with negative imaginary part is simply the complex conjugate of the eigenvector associated with the positive imaginary part.
For example, if ${\mathbf{di}}\left[0\right]$ is nonzero, the first eigenvector has real parts stored in locations ${\mathbf{z}}\left[\mathit{i}\right]$, for $\mathit{i}=0,1,\dots {\mathbf{n}}-1$ and imaginary parts stored in ${\mathbf{z}}\left[\mathit{i}\right]$, for $\mathit{i}={\mathbf{n}},\dots ,2{\mathbf{n}}-1$.
11:   resid[$\mathit{dim}$]doubleInput/Output
Note: the dimension, dim, of the array resid must be at least ${\mathbf{n}}$ (see nag_real_banded_sparse_eigensystem_init (f12afc)).
On entry: need not be set unless the option ${\mathbf{Initial Residual}}$ has been set in a prior call to nag_real_sparse_eigensystem_option (f12adc) in which case resid should contain an initial residual vector.
On exit: contains the final residual vector.
12:   v[$\mathit{dim}$]doubleOutput
Note: the dimension, dim, of the array v must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}×{\mathbf{ncv}}\right)$ (see nag_real_banded_sparse_eigensystem_init (f12afc)).
On exit: if the option ${\mathbf{Vectors}}$ (see nag_real_sparse_eigensystem_option (f12adc)) has been set to Schur or Ritz and z is not set to 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 $\mathit{i}=1,2,\dots ,n$.
13:   comm[$\mathit{dim}$]doubleCommunication Array
Note: the dimension, dim, of the array comm must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{lcomm}}\right)$ (see nag_real_banded_sparse_eigensystem_init (f12afc)).
On initial entry: must remain unchanged from the prior call to nag_real_sparse_eigensystem_option (f12adc) and nag_real_banded_sparse_eigensystem_init (f12afc).
On exit: contains no useful information.
14:   icomm[$\mathit{dim}$]IntegerCommunication Array
Note: the dimension, dim, of the array icomm must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{licomm}}\right)$ (see nag_real_banded_sparse_eigensystem_init (f12afc)).
On initial entry: must remain unchanged from the prior call to nag_real_sparse_eigensystem_option (f12adc) and nag_real_banded_sparse_eigensystem_init (f12afc).
On exit: contains no useful information.
15:   failNagError *Input/Output
The NAG error argument (see Section 3.6 in the Essential Introduction).

## 6  Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
On entry, argument $〈\mathit{\text{value}}〉$ had an illegal value.
NE_COMP_BAND_FAC
NE_COMP_BAND_SOL
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: ${\mathbf{ku}}\ge 0$.
The maximum number of iterations $\text{}\le 0$, the option ${\mathbf{Iteration Limit}}$ has been set to $〈\mathit{\text{value}}〉$.
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.
NE_INVALID_OPTION
On entry, ${\mathbf{Vectors}}=\text{Select}$, but this is not yet implemented.
NE_MAX_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_NO_ARNOLDI_FAC
Could not build an Arnoldi factorization. The size of the current Arnoldi factorization $=〈\mathit{\text{value}}〉$.
NE_NO_SHIFTS_APPLIED
No shifts could be applied during a cycle of the implicitly restarted Lanczos iteration.
NE_OPT_INCOMPAT
The options ${\mathbf{Generalized}}$ and ${\mathbf{Regular}}$ are incompatible.
NE_REAL_BAND_FAC
NE_REAL_BAND_SOL
NE_SCHUR_EIG_FAIL
During calculation of a real Schur form, there was a failure to compute a number of eigenvalues Please contact NAG.
NE_SCHUR_REORDER
NE_TRANSFORM_OVFL
Overflow occurred during transformation of Ritz values to those of the original problem.
NE_ZERO_EIGS_FOUND
The number of eigenvalues found to sufficient accuracy is zero.
NE_ZERO_INIT_RESID
The option ${\mathbf{Initial Residual}}$ was selected but the starting vector held in resid is zero.
NE_ZERO_SHIFT
The option ${\mathbf{Shifted Inverse Imaginary}}$ has been selected and ${\mathbf{sigmai}}=\text{}$ zero on entry; sigmai must be nonzero for this mode of operation.

## 7  Accuracy

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).

None.

## 9  Example

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

### 9.1  Program Text

Program Text (f12agce.c)

### 9.2  Program Data

Program Data (f12agce.d)

### 9.3  Program Results

Program Results (f12agce.r)