NAG FL Interface
f01jff (real_gen_matrix_frcht_pow)
1
Purpose
f01jff computes the Fréchet derivative $L\left(A,E\right)$ of the $p$th power (where $p$ is real) of the real $n$ by $n$ matrix $A$ applied to the real $n$ 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 
Real (Kind=nag_wp), Intent (Inout) 
:: 
a(lda,*), e(lde,*) 

C Header Interface
#include <nag.h>
void 
f01jff_ (const Integer *n, double a[], const Integer *lda, double e[], const Integer *lde, const double *p, Integer *ifail) 

C++ Header Interface
#include <nag.h> extern "C" {
void 
f01jff_ (const Integer &n, double a[], const Integer &lda, double e[], const Integer &lde, const double &p, Integer &ifail) 
}

The routine may be called by the names f01jff or nagf_matop_real_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
$\left\{z:\pi <\mathrm{Im}\left(z\right)<\pi \right\}$).
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 ${A}^{p}$.
f01jff 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:
$\mathbf{a}\left({\mathbf{lda}},*\right)$ – Real (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, ${A}^{p}$.

3:
$\mathbf{lda}$ – Integer
Input

On entry: the first dimension of the array
a as declared in the (sub)program from which
f01jff is called.
Constraint:
${\mathbf{lda}}\ge {\mathbf{n}}$.

4:
$\mathbf{e}\left({\mathbf{lde}},*\right)$ – Real (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
f01jff 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
$0$,
$1\text{or}1$. If you are unfamiliar with this argument you should refer to
Section 4 in the Introduction to the NAG Library FL Interface for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value
$1\text{or}1$ is recommended. If the output of error messages is undesirable, then the value
$1$ is recommended. Otherwise, if you are not familiar with this argument, the recommended value is
$0$.
When the value $\mathbf{1}\text{or}\mathbf{1}$ 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;
f01kff can be used to find a complex, nonprincipal
$p$th power.
 ${\mathbf{ifail}}=2$

$A$ 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
${A}^{\mathrm{T}}A=A{A}^{\mathrm{T}}$), 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
f01jef should be used.
8
Parallelism and Performance
f01jff is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f01jff 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 real 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
f01eqf should be used. If the condition number of the matrix power is required then
f01jef should be used. If
$A$ has negative real eigenvalues then
f01kff can be used to return a complex, nonprincipal
$p$th power and its Fréchet derivative
$L\left(A,E\right)$.
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