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Chapter Introduction
NAG Toolbox

NAG Toolbox: nag_matop_complex_gen_matrix_frcht_log (f01kk)

 Contents

    1  Purpose
    2  Syntax
    7  Accuracy
    9  Example

Purpose

nag_matop_complex_gen_matrix_frcht_log (f01kk) computes the Fréchet derivative LA,E of the matrix logarithm of the complex n by n matrix A applied to the complex n by n matrix E. The principal matrix logarithm logA is also returned.

Syntax

[a, e, ifail] = f01kk(a, e, 'n', n)
[a, e, ifail] = nag_matop_complex_gen_matrix_frcht_log(a, e, 'n', n)

Description

For a matrix with no eigenvalues on the closed negative real line, the principal matrix logarithm logA is the unique logarithm whose spectrum lies in the strip z:-π<Imz<π.
The Fréchet derivative of the matrix logarithm of A is the unique linear mapping ELA,E such that for any matrix E 
logA+E - logA - LA,E = oE .  
The derivative describes the first order effect of perturbations in A on the logarithm logA.
nag_matop_complex_gen_matrix_frcht_log (f01kk) uses the algorithm of Al–Mohy et al. (2012) to compute logA and LA,E. The principal matrix logarithm logA is computed using a Schur decomposition, a Padé approximant and the inverse scaling and squaring method. The Padé approximant is then differentiated in order to obtain the Fréchet derivative LA,E. If A is nonsingular but has negative real eigenvalues, the principal logarithm is not defined, but nag_matop_complex_gen_matrix_frcht_log (f01kk) will return a non-principal logarithm and Fréchet derivative.

References

Al–Mohy A H and Higham N J (2011) Improved inverse scaling and squaring algorithms for the matrix logarithm SIAM J. Sci. Comput. 34(4) C152–C169
Al–Mohy A H, Higham N J and Relton S D (2012) Computing the Fréchet derivative of the matrix logarithm and estimating the condition number MIMS EPrint 2012.72
Higham N J (2008) Functions of Matrices: Theory and Computation SIAM, Philadelphia, PA, USA

Parameters

Compulsory Input Parameters

1:     alda: – complex 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:     elde: – complex 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 and the second dimension of the arrays a, e. (An error is raised if these dimensions are not equal.)
n, the order of the matrix A.
Constraint: n0.

Output Parameters

1:     alda: – complex 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 principal matrix logarithm, logA. Alterntively, if ifail=2, a non-principal logarithm is returned.
2:     elde: – complex array
The first dimension of the array e will be n.
The second dimension of the array e will be n.
With ifail=0, 2 or 3, the Fréchet derivative LA,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
A is singular so the logarithm cannot be computed.
   ifail=2
A has eigenvalues on the negative real line. The principal logarithm is not defined in this case, so a non-principal logarithm was returned.
   ifail=3
logA has been computed using an IEEE double precision Padé approximant, although the arithmetic precision is higher than IEEE double precision.
   ifail=4
An unexpected internal error occurred. This failure should not occur and suggests that the function has been called incorrectly.
   ifail=-1
Constraint: n0.
   ifail=-3
Constraint: ldan.
   ifail=-5
Constraint: lden.
   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 AHA=AAH), the Schur decomposition is diagonal and the computation of the matrix logarithm reduces to evaluating the logarithm of the eigenvalues of A and then constructing logA using the Schur vectors. This should give a very accurate result. In general, however, no error bounds are available for the algorithm. The sensitivity of the computation of logA and LA,E is worst when A has an eigenvalue of very small modulus or has a complex conjugate pair of eigenvalues lying close to the negative real axis. See Al–Mohy and Higham (2011), Al–Mohy et al. (2012) and Section 11.2 of Higham (2008) for details and further discussion.

Further Comments

The cost of the algorithm is On3 floating-point operations. The complex allocatable memory required is approximately 5n2; see Al–Mohy et al. (2012) for further details.
If the matrix logarithm alone is required, without the Fréchet derivative, then nag_matop_complex_gen_matrix_log (f01fj) should be used. If the condition number of the matrix logarithm is required then nag_matop_complex_gen_matrix_cond_log (f01kj) should be used. The real analogue of this function is nag_matop_real_gen_matrix_frcht_log (f01jk).

Example

This example finds the principal matrix logarithm logA and the Fréchet derivative LA,E, where
A = 1+4i 3i 0i 2i+ 2i 3i+0 1i+0 1+i 0i 2+0i 2i+0 i 1+2i 3+2i 1+2i 3+i   and   E = 1i+0 1+2i 2i+0 2+i 1+3i 0i 1i+0 0i+ 2i 4+0i 1i+0 1i+ 1i+0 2+2i 3i 1i+ .  
function f01kk_example


fprintf('f01kk example results\n\n');

% Logarithm of matrix A and Frechet derivative of log(A)E.

a = [ 1+4i    3i     i  2;
        2i  3     1     1+i;
      i     2+ i  2       i;
      1+2i  3+2i  1+2i  3+i];

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

[loga, lae, ifail] = f01kk(a,e);

[ifail] = x04da('General', ' ', loga, 'log(A):');
disp(' ');
[ifail] = x04da('General', ' ', lae, 'L_log(A,E):');


f01kk example results

 log(A):
             1          2          3          4
 1      1.4188     0.2758    -0.2240     0.4528
        1.2438     1.0040     0.0826    -0.5887

 2      0.2299     1.0702     0.5292     0.1976
        0.4825    -0.3306    -0.0422     0.1532

 3      0.1328     0.9235     0.6051    -0.1211
       -0.0462     0.3060    -0.0973     0.2966

 4      0.4704     1.0779     0.2724     0.9612
       -0.0891     0.0538     0.7627     0.2680
 
 L_log(A,E):
             1          2          3          4
 1      0.1620    -0.0593    -0.1543     0.5534
       -0.6532     0.8434    -1.3537     0.0869

 2      0.6673     0.0637     0.3421    -0.4639
        0.7351    -0.0911     0.1136    -0.3399

 3     -0.2500     1.4898    -0.1547     0.3319
       -0.0433     0.6186    -0.0495    -0.3078

 4     -0.4004     0.5834    -0.5153     0.4407
       -0.5893    -0.5926     1.4107     0.1236

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