NAG FL Interface
f01kff (complex_gen_matrix_frcht_pow)
1
Purpose
f01kff computes the Fréchet derivative $L\left(A,E\right)$ of the $p$th power (where $p$ is real) of the complex $n$ by $n$ matrix $A$ applied to the complex $n$ by $n$ matrix $E$. The principal matrix power ${A}^{p}$ is also returned.
2
Specification
Fortran Interface
Integer, Intent (In) 
:: 
n, lda, lde 
Integer, Intent (Inout) 
:: 
ifail 
Real (Kind=nag_wp), Intent (In) 
:: 
p 
Complex (Kind=nag_wp), Intent (Inout) 
:: 
a(lda,*), e(lde,*) 

C Header Interface
#include <nag.h>
void 
f01kff_ (const Integer *n, Complex a[], const Integer *lda, Complex e[], const Integer *lde, const double *p, Integer *ifail) 

C++ Header Interface
#include <nag.h> extern "C" {
void 
f01kff_ (const Integer &n, Complex a[], const Integer &lda, Complex e[], const Integer &lde, const double &p, Integer &ifail) 
}

The routine may be called by the names f01kff or nagf_matop_complex_gen_matrix_frcht_pow.
3
Description
For a matrix
$A$ with no eigenvalues on the closed negative real line,
${A}^{p}$ (
$p\in \mathbb{R}$) can be defined as
where
$\mathrm{log}\left(A\right)$ is the principal logarithm of
$A$ (the unique logarithm whose spectrum lies in the strip
$\left\{z:\pi <\mathrm{Im}\left(z\right)<\pi \right\}$). If
$A$ is nonsingular but has negative real eigenvalues, the principal logarithm is not defined, but a nonprincipal
$p$th power can be defined by using a nonprincipal logarithm.
The Fréchet derivative of the matrix
$p$th power 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 matrix power ${A}^{p}$.
f01kff uses the algorithms of
Higham and Lin (2011) and
Higham and Lin (2013) to compute
${A}^{p}$ and
$L\left(A,E\right)$. The real number
$p$ is expressed as
$p=q+r$ where
$q\in \left(1,1\right)$ and
$r\in \mathbb{Z}$. Then
${A}^{p}={A}^{q}{A}^{r}$. The integer power
${A}^{r}$ is found using a combination of binary powering and, if necessary, matrix inversion. The fractional power
${A}^{q}$ is computed using a Schur decomposition, a Padé approximant and the scaling and squaring method. The Padé approximant is differentiated in order to obtain the Fréchet derivative of
${A}^{q}$ and
$L\left(A,E\right)$ is then computed using a combination of the chain rule and the product rule for Fréchet derivatives.
4
References
Higham N J (2008) Functions of Matrices: Theory and Computation SIAM, Philadelphia, PA, USA
Higham N J and Lin L (2011) A Schur–Padé algorithm for fractional powers of a matrix SIAM J. Matrix Anal. Appl. 32(3) 1056–1078
Higham N J and Lin L (2013) An improved Schur–Padé algorithm for fractional powers of a matrix and their Fréchet derivatives SIAM J. Matrix Anal. Appl. 34(3) 1341–1360
5
Arguments

1:
$\mathbf{n}$ – Integer
Input

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

2:
$\mathbf{a}\left({\mathbf{lda}},*\right)$ – Complex (Kind=nag_wp) array
Input/Output

Note: the second dimension of the array
a
must be at least
${\mathbf{n}}$.
On entry: the $n$ by $n$ matrix $A$.
On exit: the $n$ by $n$ principal matrix $p$th power, ${A}^{p}$. Alternatively if ${\mathbf{ifail}}={\mathbf{1}}$, a nonprincipal $p$th power is returned.

3:
$\mathbf{lda}$ – Integer
Input

On entry: the first dimension of the array
a as declared in the (sub)program from which
f01kff is called.
Constraint:
${\mathbf{lda}}\ge {\mathbf{n}}$.

4:
$\mathbf{e}\left({\mathbf{lde}},*\right)$ – Complex (Kind=nag_wp) array
Input/Output

Note: the second dimension of the array
e
must be at least
${\mathbf{n}}$.
On entry: the $n$ by $n$ matrix $E$.
On exit: the Fréchet derivative $L\left(A,E\right)$.

5:
$\mathbf{lde}$ – Integer
Input

On entry: the first dimension of the array
e as declared in the (sub)program from which
f01kff is called.
Constraint:
${\mathbf{lde}}\ge {\mathbf{n}}$.

6:
$\mathbf{p}$ – Real (Kind=nag_wp)
Input

On entry: the required power of $A$.

7:
$\mathbf{ifail}$ – Integer
Input/Output

On entry:
ifail must be set to
$0$,
$1$ or
$1$ to set behaviour on detection of an error; these values have no effect when no error is detected.
A value of $0$ causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of $1$ means that an error message is printed while a value of $1$ means that it is not.
If halting is not appropriate, the value
$1$ or
$1$ is recommended. If message printing is undesirable, then the value
$1$ is recommended. Otherwise, the value
$0$ is recommended.
When the value $\mathbf{1}$ or $\mathbf{1}$ is used it is essential to test the value of ifail on exit.
On exit:
${\mathbf{ifail}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see
Section 6).
6
Error Indicators and Warnings
If on entry
${\mathbf{ifail}}=0$ or
$1$, explanatory error messages are output on the current error message unit (as defined by
x04aaf).
Errors or warnings detected by the routine:
 ${\mathbf{ifail}}=1$

$A$ has eigenvalues on the negative real line. The principal $p$th power is not defined in this case, so a nonprincipal power was returned.
 ${\mathbf{ifail}}=2$

$A$ is singular so the $p$th power cannot be computed.
 ${\mathbf{ifail}}=3$

${A}^{p}$ has been computed using an IEEE double precision Padé approximant, although the arithmetic precision is higher than IEEE double precision.
 ${\mathbf{ifail}}=4$

An unexpected internal error occurred. This failure should not occur and suggests that the routine has been called incorrectly.
 ${\mathbf{ifail}}=1$

On entry, ${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{n}}\ge 0$.
 ${\mathbf{ifail}}=3$

On entry, ${\mathbf{lda}}=\u2329\mathit{\text{value}}\u232a$ and ${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{lda}}\ge {\mathbf{n}}$.
 ${\mathbf{ifail}}=5$

On entry, ${\mathbf{lde}}=\u2329\mathit{\text{value}}\u232a$ and ${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{lde}}\ge {\mathbf{n}}$.
 ${\mathbf{ifail}}=99$
An unexpected error has been triggered by this routine. Please
contact
NAG.
See
Section 7 in the Introduction to the NAG Library FL Interface for further information.
 ${\mathbf{ifail}}=399$
Your licence key may have expired or may not have been installed correctly.
See
Section 8 in the Introduction to the NAG Library FL Interface for further information.
 ${\mathbf{ifail}}=999$
Dynamic memory allocation failed.
See
Section 9 in the Introduction to the NAG Library FL Interface for further information.
7
Accuracy
For a normal matrix
$A$ (for which
${A}^{\mathrm{H}}A=A{A}^{\mathrm{H}}$), the Schur decomposition is diagonal and the computation of the fractional part of the matrix power reduces to evaluating powers of the eigenvalues of
$A$ and then constructing
${A}^{p}$ using the Schur vectors. This should give a very accurate result. In general, however, no error bounds are available for the algorithm. See
Higham and Lin (2011) and
Higham and Lin (2013) for details and further discussion.
If the condition number of the matrix power is required then
f01kef should be used.
8
Parallelism and Performance
f01kff is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f01kff 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 routine. Please also consult the
Users' Note for your implementation for any additional implementationspecific information.
The complex allocatable memory required by the algorithm is approximately $6\times {n}^{2}$.
The cost of the algorithm is
$O\left({n}^{3}\right)$ floatingpoint operations; see
Higham and Lin (2011) and
Higham and Lin (2013).
If the matrix
$p$th power alone is required, without the Fréchet derivative, then
f01fqf should be used. If the condition number of the matrix power is required then
f01kef should be used. The real analogue of this routine is
f01jff.
10
Example
This example finds
${A}^{p}$ and the Fréchet derivative of the matrix power
$L\left(A,E\right)$, where
$p=0.2$,
10.1
Program Text
10.2
Program Data
10.3
Program Results