NAG Library Routine Document
f01ecf (real_gen_matrix_exp)
1
Purpose
f01ecf computes the matrix exponential, ${e}^{A}$, of a real $n$ by $n$ matrix $A$.
2
Specification
Fortran Interface
Integer, Intent (In)  ::  n, lda  Integer, Intent (Inout)  ::  ifail  Real (Kind=nag_wp), Intent (Inout)  ::  a(lda,*) 

C Header Interface
#include <nagmk26.h>
void 
f01ecf_ (const Integer *n, double a[], const Integer *lda, Integer *ifail) 

3
Description
${e}^{A}$ is computed using a Padé approximant and the scaling and squaring method described in
Al–Mohy and Higham (2009).
4
References
Al–Mohy A H and Higham N J (2009) A new scaling and squaring algorithm for the matrix exponential SIAM J. Matrix Anal. 31(3) 970–989
Higham N J (2005) The scaling and squaring method for the matrix exponential revisited SIAM J. Matrix Anal. Appl. 26(4) 1179–1193
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: $\mathbf{n}$ – IntegerInput

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) arrayInput/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 $n$ matrix exponential ${e}^{A}$.
 3: $\mathbf{lda}$ – IntegerInput

On entry: the first dimension of the array
a as declared in the (sub)program from which
f01ecf is called.
Constraint:
${\mathbf{lda}}\ge {\mathbf{n}}$.
 4: $\mathbf{ifail}$ – IntegerInput/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 3.4 in How to Use the NAG Library and its Documentation 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:
 ${\mathbf{ifail}}=1$

The linear equations to be solved for the Padé approximant are singular; it is likely that this routine has been called incorrectly.
 ${\mathbf{ifail}}=2$

The linear equations to be solved are nearly singular and the Padé approximant probably has no correct figures; it is likely that this routine has been called incorrectly.
 ${\mathbf{ifail}}=3$

${e}^{A}$ 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 has occurred. Please contact
NAG.
 ${\mathbf{ifail}}=1$

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: ${\mathbf{lda}}\ge {\mathbf{n}}$.
 ${\mathbf{ifail}}=99$
An unexpected error has been triggered by this routine. Please
contact
NAG.
See
Section 3.9 in How to Use the NAG Library and its Documentation for further information.
 ${\mathbf{ifail}}=399$
Your licence key may have expired or may not have been installed correctly.
See
Section 3.8 in How to Use the NAG Library and its Documentation for further information.
 ${\mathbf{ifail}}=999$
Dynamic memory allocation failed.
See
Section 3.7 in How to Use the NAG Library and its Documentation for further information.
7
Accuracy
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 nonnormal matrices. See
Al–Mohy and Higham (2009) and Section 10.3 of
Higham (2008) for details and further discussion.
If estimates of the condition number of the matrix exponential are required then
f01jgf should be used.
8
Parallelism and Performance
f01ecf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f01ecf 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 integer allocatable memory required is
n, and the real allocatable memory required is approximately
$6\times {{\mathbf{n}}}^{2}$.
The cost of the algorithm is
$O\left({n}^{3}\right)$; see Section 5 of of
Al–Mohy and Higham (2009). The real allocatable memory required is approximately
$6\times {n}^{2}$.
If the Fréchet derivative of the matrix exponential is required then
f01jhf should be used.
As well as the excellent book cited above, the classic reference for the computation of the matrix exponential is
Moler and Van Loan (2003).
10
Example
This example finds the matrix exponential of the matrix
10.1
Program Text
Program Text (f01ecfe.f90)
10.2
Program Data
Program Data (f01ecfe.d)
10.3
Program Results
Program Results (f01ecfe.r)