# NAG FL Interfacef01jhf (real_​gen_​matrix_​frcht_​exp)

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## 1Purpose

f01jhf computes the Fréchet derivative $L\left(A,E\right)$ of the matrix exponential of a real $n×n$ matrix $A$ applied to the real $n×n$ matrix $E$. The matrix exponential ${e}^{A}$ is also returned.

## 2Specification

Fortran Interface
 Subroutine f01jhf ( n, a, lda, e, lde,
 Integer, Intent (In) :: n, lda, lde Integer, Intent (Inout) :: ifail Real (Kind=nag_wp), Intent (Inout) :: a(lda,*), e(lde,*)
#include <nag.h>
 void f01jhf_ (const Integer *n, double a[], const Integer *lda, double e[], const Integer *lde, Integer *ifail)
The routine may be called by the names f01jhf or nagf_matop_real_gen_matrix_frcht_exp.

## 3Description

The Fréchet derivative of the matrix exponential of $A$ is the unique linear mapping $E⟼L\left(A,E\right)$ such that for any matrix $E$
 $eA+E - e A - L(A,E) = o(‖E‖) .$
The derivative describes the first-order effect of perturbations in $A$ on the exponential ${e}^{A}$.
f01jhf uses the algorithms of Al–Mohy and Higham (2009a) and Al–Mohy and Higham (2009b) to compute ${e}^{A}$ and $L\left(A,E\right)$. The matrix exponential ${e}^{A}$ is computed using a Padé approximant and the scaling and squaring method. The Padé approximant is then differentiated in order to obtain the Fréchet derivative $L\left(A,E\right)$.

## 4References

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, twenty-five years later SIAM Rev. 45 3–49

## 5Arguments

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 $n×n$ matrix $A$.
On exit: the $n×n$ matrix exponential ${e}^{A}$.
3: $\mathbf{lda}$Integer Input
On entry: the first dimension of the array a as declared in the (sub)program from which f01jhf 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 $n×n$ 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 f01jhf is called.
Constraint: ${\mathbf{lde}}\ge {\mathbf{n}}$.
6: $\mathbf{ifail}$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).

## 6Error 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:
${\mathbf{ifail}}=1$
The linear equations to be solved for the Padé approximant are singular; it is likely that this routine has been called incorrectly.
${\mathbf{ifail}}=2$
${e}^{A}$ has been computed using an IEEE double precision Padé approximant, although the arithmetic precision is higher than IEEE double precision.
${\mathbf{ifail}}=3$
${\mathbf{ifail}}=-1$
On entry, ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{n}}\ge 0$.
${\mathbf{ifail}}=-3$
On entry, ${\mathbf{lda}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{lda}}\ge {\mathbf{n}}$.
${\mathbf{ifail}}=-5$
On entry, ${\mathbf{lde}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{lde}}\ge {\mathbf{n}}$.
${\mathbf{ifail}}=-99$
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.

## 7Accuracy

For a normal matrix $A$ (for which ${A}^{\mathrm{T}}A=A{A}^{\mathrm{T}}$) 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 non-normal matrices. See Section 10.3 of Higham (2008), Al–Mohy and Higham (2009a) and Al–Mohy and Higham (2009b) for details and further discussion.

## 8Parallelism and Performance

f01jhf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f01jhf 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 implementation-specific information.

The cost of the algorithm is $O\left({n}^{3}\right)$ and the real allocatable memory required is approximately $9{n}^{2}$; see Al–Mohy and Higham (2009a) and Al–Mohy and Higham (2009b).
If the matrix exponential alone is required, without the Fréchet derivative, then f01ecf should be used.
If the condition number of the matrix exponential is required then f01jgf 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).

## 10Example

This example finds the matrix exponential ${e}^{A}$ and the Fréchet derivative $L\left(A,E\right)$, where
 $A = ( 1 2 2 2 3 1 1 2 3 2 1 2 3 3 3 1 ) and E = ( 1 0 1 2 0 0 0 1 4 2 1 2 0 3 2 1 ) .$

### 10.1Program Text

Program Text (f01jhfe.f90)

### 10.2Program Data

Program Data (f01jhfe.d)

### 10.3Program Results

Program Results (f01jhfe.r)