f01 Chapter Contents
f01 Chapter Introduction
NAG Library Manual

# NAG Library Function Documentnag_matop_real_gen_matrix_frcht_pow (f01jfc)

## 1  Purpose

nag_matop_real_gen_matrix_frcht_pow (f01jfc) 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

 #include #include
 void nag_matop_real_gen_matrix_frcht_pow (Integer n, double a[], Integer pda, double e[], Integer pde, double p, NagError *fail)

## 3  Description

For a matrix $A$ with no eigenvalues on the closed negative real line, ${A}^{p}$ ($p\in ℝ$) can be defined as
 $Ap= expplogA$
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 $E⟼L\left(A,E\right)$ such that for any matrix $E$
 $A+Ep - Ap - LA,E = oE .$
The derivative describes the first-order effect of perturbations in $A$ on the matrix power ${A}^{p}$.
nag_matop_real_gen_matrix_frcht_pow (f01jfc) 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 $r\in ℤ$. 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 MIMS Eprint 2013.1 Manchester Institute for Mathematical Sciences, School of Mathematics, University of Manchester http://eprints.ma.man.ac.uk/

## 5  Arguments

1:    $\mathbf{n}$IntegerInput
On entry: $n$, the order of the matrix $A$.
Constraint: ${\mathbf{n}}\ge 0$.
2:    $\mathbf{a}\left[\mathit{dim}\right]$doubleInput/Output
Note: the dimension, dim, of the array a must be at least ${\mathbf{pda}}×{\mathbf{n}}$.
The $\left(i,j\right)$th element of the matrix $A$ is stored in ${\mathbf{a}}\left[\left(j-1\right)×{\mathbf{pda}}+i-1\right]$.
On entry: the $n$ by $n$ matrix $A$.
On exit: the $n$ by $n$ principal matrix $p$th power, ${A}^{p}$.
3:    $\mathbf{pda}$IntegerInput
On entry: the stride separating matrix row elements in the array a.
Constraint: ${\mathbf{pda}}\ge {\mathbf{n}}$.
4:    $\mathbf{e}\left[\mathit{dim}\right]$doubleInput/Output
Note: the dimension, dim, of the array e must be at least ${\mathbf{pde}}×{\mathbf{n}}$.
The $\left(i,j\right)$th element of the matrix $E$ is stored in ${\mathbf{e}}\left[\left(j-1\right)×{\mathbf{pde}}+i-1\right]$.
On entry: the $n$ by $n$ matrix $E$.
On exit: the Fréchet derivative $L\left(A,E\right)$.
5:    $\mathbf{pde}$IntegerInput
On entry: the stride separating matrix row elements in the array e.
Constraint: ${\mathbf{pde}}\ge {\mathbf{n}}$.
6:    $\mathbf{p}$doubleInput
On entry: the required power of $A$.
7:    $\mathbf{fail}$NagError *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.
See Section 3.2.1.2 in the Essential Introduction for further information.
On entry, argument $〈\mathit{\text{value}}〉$ had an illegal value.
NE_INT
On entry, ${\mathbf{n}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{n}}\ge 0$.
NE_INT_2
On entry, ${\mathbf{pda}}=〈\mathit{\text{value}}〉$ and ${\mathbf{n}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pda}}\ge {\mathbf{n}}$.
On entry, ${\mathbf{pde}}=〈\mathit{\text{value}}〉$ and ${\mathbf{n}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pde}}\ge {\mathbf{n}}$.
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 3.6.6 in the Essential Introduction for further information.
NE_NEGATIVE_EIGVAL
$A$ has eigenvalues on the negative real line. The principal $p$th power is not defined in this case; nag_matop_complex_gen_matrix_frcht_pow (f01kfc) can be used to find a complex, non-principal $p$th power.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 3.6.5 in the Essential Introduction for further information.
NE_SINGULAR
$A$ is singular so the $p$th power cannot be computed.
NW_SOME_PRECISION_LOSS
${A}^{p}$ has been computed using an IEEE double precision Padé approximant, although the arithmetic precision is higher than IEEE double precision.

## 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 nag_matop_real_gen_matrix_cond_pow (f01jec) should be used.

## 8  Parallelism and Performance

nag_matop_real_gen_matrix_frcht_pow (f01jfc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
nag_matop_real_gen_matrix_frcht_pow (f01jfc) 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.

The real allocatable memory required by the algorithm is approximately $6×{n}^{2}$.
The cost of the algorithm is $O\left({n}^{3}\right)$ floating-point 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 nag_matop_real_gen_matrix_pow (f01eqc) should be used. If the condition number of the matrix power is required then nag_matop_real_gen_matrix_cond_pow (f01jec) should be used. If $A$ has negative real eigenvalues then nag_matop_complex_gen_matrix_frcht_pow (f01kfc) can be used to return a complex, non-principal $p$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 $p=0.2$,
 $A = 3 3 2 1 3 1 0 2 1 1 4 3 3 0 3 1 and E = 1 0 2 1 0 4 5 2 1 0 0 0 2 3 3 0 .$

### 10.1  Program Text

Program Text (f01jfce.c)

### 10.2  Program Data

Program Data (f01jfce.d)

### 10.3  Program Results

Program Results (f01jfce.r)