NAG CL Interface
f01kgc (complex_gen_matrix_cond_exp)
1
Purpose
f01kgc computes an estimate of the relative condition number ${\kappa}_{\mathrm{exp}}\left(A\right)$ of the exponential of a complex $n$ by $n$ matrix $A$, in the $1$norm. The matrix exponential ${e}^{A}$ is also returned.
2
Specification
void 
f01kgc (Integer n,
Complex a[],
Integer pda,
double *condea,
NagError *fail) 

The function may be called by the names: f01kgc or nag_matop_complex_gen_matrix_cond_exp.
3
Description
The Fréchet derivative of the matrix exponential of
$A$ is the unique linear mapping
$E\u27fcL\left(A,E\right)$ such that for any matrix
$E$
The derivative describes the firstorder effect of perturbations in $A$ on the exponential ${e}^{A}$.
The relative condition number of the matrix exponential can be defined by
where
$\Vert L\left(A\right)\Vert $ is the norm of the Fréchet derivative of the matrix exponential at
$A$.
To obtain the estimate of ${\kappa}_{\mathrm{exp}}\left(A\right)$, f01kgc first estimates $\Vert L\left(A\right)\Vert $ by computing an estimate $\gamma $ of a quantity $K\in \left[{n}^{1}{\Vert L\left(A\right)\Vert}_{1},n{\Vert L\left(A\right)\Vert}_{1}\right]$, such that $\gamma \le K$.
The algorithms used to compute
${\kappa}_{\mathrm{exp}}\left(A\right)$ are detailed in the
Al–Mohy and Higham (2009a) and
Al–Mohy and Higham (2009b).
The matrix exponential ${e}^{A}$ is computed using a Padé approximant and the scaling and squaring method. The Padé approximant is differentiated to obtain the Fréchet derivatives $L\left(A,E\right)$ which are used to estimate the condition number.
4
References
Al–Mohy A H and Higham N J (2009a) A new scaling and squaring algorithm for the matrix exponential SIAM J. Matrix Anal. 31(3) 970–989
Al–Mohy A H and Higham N J (2009b) Computing the Fréchet derivative of the matrix exponential, with an application to condition number estimation SIAM J. Matrix Anal. Appl. 30(4) 1639–1657
Higham N J (2008) Functions of Matrices: Theory and Computation SIAM, Philadelphia, PA, USA
Moler C B and Van Loan C F (2003) Nineteen dubious ways to compute the exponential of a matrix, twentyfive years later SIAM Rev. 45 3–49
5
Arguments

1:
$\mathbf{n}$ – Integer
Input

On entry: $n$, the order of the matrix $A$.
Constraint:
${\mathbf{n}}\ge 0$.

2:
$\mathbf{a}\left[\mathit{dim}\right]$ – Complex
Input/Output

Note: the dimension,
dim, of the array
a
must be at least
${\mathbf{pda}}\times {\mathbf{n}}$.
The $\left(i,j\right)$th element of the matrix $A$ is stored in ${\mathbf{a}}\left[\left(j1\right)\times {\mathbf{pda}}+i1\right]$.
On entry: the $n$ by $n$ matrix $A$.
On exit: the $n$ by $n$ matrix exponential ${e}^{A}$.

3:
$\mathbf{pda}$ – Integer
Input

On entry: the stride separating matrix row elements in the array
a.
Constraint:
${\mathbf{pda}}\ge {\mathbf{n}}$.

4:
$\mathbf{condea}$ – double *
Output

On exit: an estimate of the relative condition number of the matrix exponential ${\kappa}_{\mathrm{exp}}\left(A\right)$.

5:
$\mathbf{fail}$ – NagError *
Input/Output

The NAG error argument (see
Section 7 in the Introduction to the NAG Library CL Interface).
6
Error Indicators and Warnings
 NE_ALLOC_FAIL

Dynamic memory allocation failed.
See
Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
 NE_BAD_PARAM

On entry, argument $\u2329\mathit{\text{value}}\u232a$ had an illegal value.
 NE_INT

On entry, ${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{n}}\ge 0$.
 NE_INT_2

On entry, ${\mathbf{pda}}=\u2329\mathit{\text{value}}\u232a$ and ${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{pda}}\ge {\mathbf{n}}$.
 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 7.5 in the Introduction to the NAG Library CL Interface for further information.
 NE_NO_LICENCE

Your licence key may have expired or may not have been installed correctly.
See
Section 8 in the Introduction to the NAG Library CL Interface for further information.
 NE_SINGULAR

The linear equations to be solved for the Padé approximant are singular; it is likely that this function has been called incorrectly.
 NW_SOME_PRECISION_LOSS

${e}^{A}$ has been computed using an IEEE double precision Padé approximant, although the arithmetic precision is higher than IEEE double precision.
7
Accuracy
f01kgc uses the norm estimation function
f04zdc to produce an estimate
$\gamma $ of a quantity
$K\in \left[{n}^{1}{\Vert L\left(A\right)\Vert}_{1},n{\Vert L\left(A\right)\Vert}_{1}\right]$, such that
$\gamma \le K$. For further details on the accuracy of norm estimation, see the documentation for
f04zdc.
For a normal matrix
$A$ (for which
${A}^{\mathrm{H}}A=A{A}^{\mathrm{H}}$) the computed matrix,
${e}^{A}$, is guaranteed to be close to the exact matrix, that is, the method is forward stable. No such guarantee can be given for nonnormal matrices. See Section 10.3 of
Higham (2008) for details and further discussion.
For further discussion of the condition of the matrix exponential see Section 10.2 of
Higham (2008).
8
Parallelism and Performance
f01kgc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f01kgc 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 implementationspecific information.
f01kac uses a similar algorithm to
f01kgc to compute an estimate of the
absolute condition number (which is related to the relative condition number by a factor of
$\Vert A\Vert /\Vert \mathrm{exp}\left(A\right)\Vert $). However, the required Fréchet derivatives are computed in a more efficient and stable manner by
f01kgc and so its use is recommended over
f01kac.
The cost of the algorithm is
$O\left({n}^{3}\right)$ and the complex allocatable memory required is approximately
$15{n}^{2}$; see
Al–Mohy and Higham (2009a) and
Al–Mohy and Higham (2009b) for further details.
If the matrix exponential alone is required, without an estimate of the condition number, then
f01fcc should be used. If the Fréchet derivative of the matrix exponential is required then
f01khc should be used.
As well as the excellent book
Higham (2008), the classic reference for the computation of the matrix exponential is
Moler and Van Loan (2003).
10
Example
This example estimates the relative condition number of the matrix exponential
${e}^{A}$, where
10.1
Program Text
10.2
Program Data
10.3
Program Results