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 (A, E)

References

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

Parameters

Compulsory Input Parameters

1: $a (lda :)$ – double array: The first dimension of the array a must be at least $n$ .
The second dimension of the array a must be at least $n$ .

The $n$ by $n$ matrix $A$ .
2: $e (lde :)$ – double array: The first dimension of the array e must be at least $n$ .
The second dimension of the array e must be at least $n$ .

The $n$ by $n$ matrix $E$

Optional Input Parameters

1: $n$ – int64int32nag_int scalar: Default: the first dimension of the arrays a, e. (An error is raised if these dimensions are not equal.)
$n$ , the order of the matrix $A$ .

Constraint: $n \geq 0$ .

Output Parameters

1: $a (lda :)$ – double array: The first dimension of the array a will be $n$ .
The second dimension of the array a will be $n$ .

The $n$ by $n$ matrix exponential $e^{A}$ .
2: $e (lde :)$ – double array: The first dimension of the array e will be $n$ .
The second dimension of the array e will be $n$ .

The Fréchet derivative $L (A, E)$
3: $ifail$ – int64int32nag_int scalar: $ifail = 0$ unless the function detects an error (see Error Indicators and Warnings).

Error Indicators and Warnings

Errors or warnings detected by the function:

$ifail = 1$: The linear equations to be solved for the Padé approximant are singular; it is likely that this function has been called incorrectly.

$ifail = 2$: $e^{A}$ has been computed using an IEEE double precision Padé approximant, although the arithmetic precision is higher than IEEE double precision.

$ifail = 3$: An unexpected internal error has occurred. Please contact NAG.

$ifail = - 1$: Constraint: $n \geq 0$ .

$ifail = - 3$: Constraint: $lda \geq n$ .

$ifail = - 5$: Constraint: $lde \geq n$ .

$ifail = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.

$ifail = - 399$: Your licence key may have expired or may not have been installed correctly.

$ifail = - 999$: Dynamic memory allocation failed.

Accuracy

For a normal matrix

A

(for which

A^{T} A = A A^{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.

Further Comments

The cost of the algorithm is

O (n^{3})

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 nag_matop_real_gen_matrix_exp (f01ec) should be used.

If the condition number of the matrix exponential is required then nag_matop_real_gen_matrix_cond_exp (f01jg) 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).

Example

This example finds the matrix exponential

e^{A}

and the Fréchet derivative

L (A, E)

, where

A = (\begin{array}{r} 1 & 2 & 2 & 2 \\ 3 & 1 & 1 & 2 \\ 3 & 2 & 1 & 2 \\ 3 & 3 & 3 & 1 \end{array}) and E = (\begin{array}{r} 1 & 0 & 1 & 2 \\ 0 & 0 & 0 & 1 \\ 4 & 2 & 1 & 2 \\ 0 & 3 & 2 & 1 \end{array}) .

Open in the MATLAB editor: f01jh_example

function f01jh_example


fprintf('f01jh example results\n\n');

% Exponential of matrix A and Frechet derivative of exp(A)E.

a = [ 1 2 2 2;
      3 1 1 2;
      3 2 1 2;
      3 3 3 1];

e = [ 1 0 1 2;
      0 0 0 1;
      4 2 1 2;
      0 3 2 1];

[expa, lae, ifail] = f01jh(a,e);

[ifail] = x04ca('General', ' ', expa, 'exp(A):');
disp(' ');
[ifail] = x04ca('General', ' ', lae, 'L_exp(A,E):');

f01jh example results

 exp(A):
             1          2          3          4
 1    740.7038   610.8500   542.2743   549.1753
 2    731.2510   603.5524   535.0884   542.2743
 3    823.7630   679.4257   603.5524   610.8500
 4    998.4355   823.7630   731.2510   740.7038
 
 L_exp(A,E):
             1          2          3          4
 1   3571.5724  2989.2581  2652.3449  2818.7416
 2   3202.0590  2684.2631  2381.4500  2542.7976
 3   4341.3950  3628.9329  3219.3516  3408.1831
 4   4821.2945  4035.9700  3580.0124  3804.4690

PDF version (NAG web site, 64-bit version, 64-bit version)

Chapter Contents

Chapter Introduction

NAG Toolbox