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

L (A, E)

4 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 SIAM J. Sci. Comput. 35(4) C394–C410

Higham N J (2008) Functions of Matrices: Theory and Computation SIAM, Philadelphia, PA, USA

5 Arguments

1: $n$ – Integer Input: On entry: $n$ , the order of the matrix $A$ .

Constraint: $n \geq 0$ .
2: $a [\dim]$ – double Input/Output: Note: the dimension, dim, of the array a must be at least $pda \times n$ .

The $(i, j)$ th element of the matrix $A$ is stored in $a [(j - 1) \times pda + i - 1]$ .

On entry: the $n \times n$ matrix $A$ .

On exit: the $n \times n$ principal matrix logarithm, $\log (A)$ .
3: $pda$ – Integer Input: On entry: the stride separating matrix row elements in the array a.

Constraint: $pda \geq n$ .
4: $e [\dim]$ – double Input/Output: Note: the dimension, dim, of the array e must be at least $pde \times n$ .

The $(i, j)$ th element of the matrix $E$ is stored in $e [(j - 1) \times pde + i - 1]$ .

On entry: the $n \times n$ matrix $E$

On exit: the Fréchet derivative $L (A, E)$
5: $pde$ – Integer Input: On entry: the stride separating matrix row elements in the array e.

Constraint: $pde \geq n$ .
6: $fail$ – NagError * Input/Output: The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_BAD_PARAM: On entry, argument $⟨ value ⟩$ had an illegal value.
NE_INT: On entry, $n = ⟨ value ⟩$ .
Constraint: $n \geq 0$ .
NE_INT_2: On entry, $pda = ⟨ value ⟩$ and $n = ⟨ value ⟩$ .
Constraint: $pda \geq n$ .

On entry, $pde = ⟨ value ⟩$ and $n = ⟨ value ⟩$ .
Constraint: $pde \geq n$ .
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 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_NEGATIVE_EIGVAL: $A$ has eigenvalues on the negative real line. The principal logarithm is not defined in this case; f01kkc can be used to return a complex, non-principal log.
NE_NO_LICENCE: Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
NE_SINGULAR: $A$ is singular so the logarithm cannot be computed.
NW_SOME_PRECISION_LOSS: $\log (A)$ 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^{T} A = A A^{T}

), 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

\log (A)

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

\log (A)

and

L (A, 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.

8 Parallelism and Performance

f01jkc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

f01jkc 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.

9 Further Comments

The cost of the algorithm is

O (n^{3})

floating-point operations. The real allocatable memory required is approximately

5 n^{2}

; see Al–Mohy et al. (2012) for further details.

If the matrix logarithm alone is required, without the Fréchet derivative, then f01ejc should be used. If the condition number of the matrix logarithm is required then f01jjc should be used. If

A

has negative real eigenvalues then f01kkc can be used to return a complex, non-principal matrix logarithm and its Fréchet derivative

L (A, E)

10 Example

This example finds the principal matrix logarithm

\log (A)

and the Fréchet derivative

L (A, E)

, where

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

f01jk: FL CL CPP AD

NAG CL Interfacef01jkc (real_​gen_​matrix_​frcht_​log)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG CL Interface
f01jkc (real_gen_matrix_frcht_log)