NAG Library Routine Document
F01FLF
1 Purpose
F01FLF computes the matrix function, $f\left(A\right)$, of a complex $n$ by $n$ matrix $A$. Numerical differentiation is used to evaluate the derivatives of $f$ when they are required.
2 Specification
INTEGER 
N, LDA, IUSER(*), IFLAG, IFAIL 
REAL (KIND=nag_wp) 
RUSER(*) 
COMPLEX (KIND=nag_wp) 
A(LDA,*) 
EXTERNAL 
F 

3 Description
$f\left(A\right)$ is computed using the Schur–Parlett algorithm described in
Higham (2008) and
Davies and Higham (2003). The coefficients of the Taylor series used in the algorithm are evaluated using the numerical differentiation algorithm of
Lyness and Moler (1967).
The scalar function
$f$ is supplied via subroutine
F which evaluates
$f\left({z}_{i}\right)$ at a number of points
${z}_{i}$.
4 References
Davies P I and Higham N J (2003) A Schur–Parlett algorithm for computing matrix functions. SIAM J. Matrix Anal. Appl. 25(2) 464–485
Higham N J (2008) Functions of Matrices: Theory and Computation SIAM, Philadelphia, PA, USA
Lyness J N and Moler C B (1967) Numerical differentiation of analytic functions SIAM J. Numer. Anal. 4(2) 202–210
5 Parameters
 1: N – INTEGERInput
On entry: $n$, the order of the matrix $A$.
Constraint:
${\mathbf{N}}\ge 0$.
 2: A(LDA,$*$) – COMPLEX (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, $f\left(A\right)$.
 3: LDA – INTEGERInput
On entry: the first dimension of the array
A as declared in the (sub)program from which F01FLF is called.
Constraint:
${\mathbf{LDA}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{N}}\right)$.
 4: F – SUBROUTINE, supplied by the user.External Procedure
The subroutine
F evaluates
$f\left({z}_{i}\right)$ at a number of points
${z}_{i}$.
The specification of
F is:
INTEGER 
IFLAG, NZ, IUSER(*) 
REAL (KIND=nag_wp) 
RUSER(*) 
COMPLEX (KIND=nag_wp) 
Z(NZ), FZ(NZ) 

 1: IFLAG – INTEGERInput/Output
On entry:
IFLAG will be zero.
On exit:
IFLAG should either be unchanged from its entry value of zero, or may be set nonzero to indicate that there is a problem in evaluating the function
$f\left({z}_{i}\right)$; for instance
$f\left({z}_{i}\right)$ may not be defined. If
IFLAG is returned as nonzero then F01FLF will terminate the computation, with
${\mathbf{IFAIL}}={\mathbf{2}}$.
 2: NZ – INTEGERInput
On entry: ${n}_{z}$, the number of function values required.
 3: Z(NZ) – COMPLEX (KIND=nag_wp) arrayInput
On entry: the ${n}_{z}$ points ${z}_{1},{z}_{2},\dots ,{z}_{{n}_{z}}$ at which the function $f$ is to be evaluated.
 4: FZ(NZ) – COMPLEX (KIND=nag_wp) arrayOutput
On exit: the ${n}_{z}$ function values.
${\mathbf{FZ}}\left(\mathit{i}\right)$ should return the value $f\left({z}_{\mathit{i}}\right)$, for $\mathit{i}=1,2,\dots ,{n}_{z}$.
 5: IUSER($*$) – INTEGER arrayUser Workspace
 6: RUSER($*$) – REAL (KIND=nag_wp) arrayUser Workspace

F is called with the parameters
IUSER and
RUSER as supplied to F01FLF. You are free to use the arrays
IUSER and
RUSER to supply information to
F as an alternative to using COMMON global variables.
F must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which F01FLF is called. Parameters denoted as
Input must
not be changed by this procedure.
 5: IUSER($*$) – INTEGER arrayUser Workspace
 6: RUSER($*$) – REAL (KIND=nag_wp) arrayUser Workspace

IUSER and
RUSER are not used by F01FLF, but are passed directly to
F and may be used to pass information to this routine as an alternative to using COMMON global variables.
 7: IFLAG – INTEGEROutput
On exit:
${\mathbf{IFLAG}}=0$, unless
IFLAG has been set nonzero inside
F, in which case
IFLAG will be the value set and
IFAIL will be set to
${\mathbf{IFAIL}}={\mathbf{2}}$.
 8: IFAIL – INTEGERInput/Output

On entry:
IFAIL must be set to
$0$,
$1\text{ or}1$. If you are unfamiliar with this parameter you should refer to
Section 3.3 in the Essential Introduction 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 parameter, 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
${\mathbf{IFAIL}}={\mathbf{0}}$ or
${{\mathbf{1}}}$, explanatory error messages are output on the current error message unit (as defined by
X04AAF).
Errors or warnings detected by the routine:
 ${\mathbf{IFAIL}}=1$

A Taylor series failed to converge after $40$ terms. Further Taylor series coefficients can no longer reliably be obtained by numerical differentiation.
 ${\mathbf{IFAIL}}=2$

IFLAG has been set nonzero by the user.
 ${\mathbf{IFAIL}}=3$

The function was unable to compute the Schur decomposition of $A$.
Note: this failure should not occur and suggests that the routine has been called incorrectly.
There was an error whilst reordering the Schur form of $A$.
Note: this failure should not occur and suggests that the routine has been called incorrectly.
 ${\mathbf{IFAIL}}=5$

An unexpected internal error occurred. Please contact
NAG.
 ${\mathbf{IFAIL}}=1$

Input argument number $\u27e8\mathit{\text{value}}\u27e9$ is invalid.
 ${\mathbf{IFAIL}}=3$

On entry, parameter
LDA is invalid.
Constraint:
${\mathbf{LDA}}\ge {\mathbf{N}}$.
 ${\mathbf{IFAIL}}=999$

Allocation of memory failed. Up to $6\times {N}^{2}$ of
complex
allocatable memory may be required.
7 Accuracy
For a normal matrix
$A$ (for which
${A}^{\mathrm{H}}A=A{A}^{\mathrm{H}}$) Schur decomposition is diagonal and the algorithm reduces to evaluating
$f$ at the eigenvalues of
$A$ and then constructing
$f\left(A\right)$ using the Schur vectors. See Section 9.4 of
Higham (2008) for further discussion of the Schur–Parlett algorithm, and
Lyness and Moler (1967) for discussion of the numerical differentiation subroutine.
The integer allocatable memory required is $n$, and up to $6{n}^{2}$ of
complex
allocatable memory is required.
The cost of the Schur–Parlett algorithm depends on the spectrum of
$A$, but is roughly between
$28{n}^{3}$ and
${n}^{4}/3$ floating point operations. There is an additional cost in numerically differentiating
$f$, in order to obtain the Taylor series coefficients. If the derivatives of
$f$ are known analytically, then
F01FMF can be used to evaluate
$f\left(A\right)$ more accurately. If
$A$ is complex Hermitian then it is recommended that
F01FFF be used as it is more efficient and, in general, more accurate than F01FLF.
Note that $f$ must be analytic in the region of the complex plane containing the spectrum of $A$.
For further information on matrix functions, see
Higham (2008).
If estimates of the condition number of the matrix function are required then
F01KBF should be used.
F01ELF can be used to find the matrix function
$f\left(A\right)$ for a real matrix
$A$.
9 Example
This example finds
$\mathrm{sin}2A$ where
9.1 Program Text
Program Text (f01flfe.f90)
9.2 Program Data
Program Data (f01flfe.d)
9.3 Program Results
Program Results (f01flfe.r)