NAG Library Manual, Mark 27.2

Interfaces: FL CL CPP AD

NAG CL Interface Introduction

F01 (Matop) Chapter Contents

F01 (Matop) Chapter Introduction

f01fl: FL CL CPP AD

NAG CL Interface
f01flc (complex_gen_matrix_fun_num)

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NAG Library Manual, Mark 27.2

Interfaces: FL CL CPP AD

NAG CL Interface Introduction

F01 (Matop) Chapter Contents

F01 (Matop) Chapter Introduction

f01fl: FL CL CPP AD

▸▿ Contents

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

8 Parallelism and Performance

9 Further Comments

▸▿ 10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

© The Numerical Algorithms Group Ltd. 2021

CL Name Style:
Short (a00aac)
Long (impl_details)
Full (nag_info_impl_details)

1 Purpose

f01flc computes the matrix function,

f (A)

, of a complex

n \times n

matrix

A

. Numerical differentiation is used to evaluate the derivatives of

f

when they are required.

2 Specification

#include <nag.h>

void

f01flc (Integer n, Complex a[], Integer pda,

void	(f)(Integer iflag, Integer nz, const Complex z[], Complex fz[], Nag_Comm *comm),

Nag_Comm *comm, Integer *iflag, NagError *fail)

The function may be called by the names: f01flc or nag_matop_complex_gen_matrix_fun_num.

3 Description

f (A)

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 function f which evaluates

f (z_{i})

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 Arguments

1: $n$ – Integer Input

On entry:

n

, the order of the matrix

A

.

Constraint:

n \geq 0

.

2: $a [\dim]$ – Complex 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

matrix,

f (A)

.

3: $pda$ – Integer Input

On entry: the stride separating matrix row elements in the array a.

Constraint:

pda \geq n

.

4: $f$ – function, supplied by the user External Function

The function f evaluates

f (z_{i})

at a number of points

z_{i}

.

The specification of f is:

void	f (Integer iflag, Integer nz, const Complex z[], Complex fz[], Nag_Comm comm)

1: $iflag$ – Integer * Input/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 (z_{i})

; for instance

f (z_{i})

may not be defined. If iflag is returned as nonzero then f01flc will terminate the computation, with

fail . code =

NE_INT, NE_INT_2 or NE_USER_STOP.

2: $nz$ – Integer Input

On entry:

n_{z}

, the number of function values required.

3: $z [\dim]$ – const Complex Input

On entry: the

n_{z}

points

z_{1}, z_{2}, \dots, z_{n_{z}}

at which the function

f

is to be evaluated.

4: $fz [\dim]$ – Complex Output

On exit: the

n_{z}

function values.

fz [i - 1]

should return the value

f (z_{i})

, for

i = 1, 2, \dots, n_{z}

.

5: $comm$ – Nag_Comm *

Pointer to structure of type Nag_Comm; the following members are relevant to f.

user – double *
iuser – Integer *
p – Pointer: The type Pointer will be void *. Before calling f01flc you may allocate memory and initialize these pointers with various quantities for use by f when called from f01flc (see Section 3.1.1 in the Introduction to the NAG Library CL Interface).

Note: f should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by f01flc. If your code inadvertently does return any NaNs or infinities, f01flc is likely to produce unexpected results.

5: $comm$ – Nag_Comm *

The NAG communication argument (see Section 3.1.1 in the Introduction to the NAG Library CL Interface).

6: $iflag$ – Integer * Output

On exit:

iflag = 0

, unless iflag has been set nonzero inside f, in which case iflag will be the value set and fail will be set to

fail . code =

NE_INT, NE_INT_2 or NE_USER_STOP.

7: $fail$ – 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_CONVERGENCE: A Taylor series failed to converge after $40$ terms. Further Taylor series coefficients can no longer reliably be obtained by numerical differentiation.
NE_INT: On entry, $n = ⟨ value ⟩$ .
Constraint: $n \geq 0$ .
NE_INT_2: On entry, $pda = ⟨ value ⟩$ and $n = ⟨ value ⟩$ .
Constraint: $pda \geq n$ .
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.

An unexpected internal error occurred when ordering the eigenvalues of $A$ . Please contact NAG.

The function was unable to compute the Schur decomposition of $A$ .
Note: this failure should not occur and suggests that the function 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 function has been called incorrectly.
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_USER_STOP: Termination requested in f.

7 Accuracy

For a normal matrix

A

(for which

A^{H} A = A A^{H}

) Schur decomposition is diagonal and the algorithm reduces to evaluating

f

at the eigenvalues of

A

and then constructing

f (A)

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 a discussion of numerical differentiation.

8 Parallelism and Performance

f01flc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library. In these implementations, this function may make calls to the user-supplied functions from within an OpenMP parallel region. Thus OpenMP pragmas within the user functions can only be used if you are compiling the user-supplied function and linking the executable in accordance with the instructions in the Users' Note for your implementation. You must also ensure that you use the NAG communication argument comm in a thread safe manner, which is best achieved by only using it to supply read-only data to the user functions.

f01flc 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 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 f01fmc can be used to evaluate

f (A)

more accurately. If

A

is complex Hermitian then it is recommended that f01ffc be used as it is more efficient and, in general, more accurate than f01flc.

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 f01kbc should be used.

f01elc can be used to find the matrix function

f (A)

for a real matrix

A

.

10 Example

This example finds

\sin 2 A

where

A = (\begin{array}{r} 1.0 + 0.0 i & 0.0 + 1.0 i & 1.0 + 0.0 i & 0.0 + 1.0 i \\ - 1.0 + 0.0 i & 0.0 + 0.0 i & 2.0 + 1.0 i & 0.0 + 0.0 i \\ 0.0 + 0.0 i & 2.0 + 1.0 i & 0.0 + 2.0 i & 0.0 + 1.0 i \\ 1.0 + 0.0 i & 1.0 + 1.0 i & - 1.0 + 0.0 i & 2.0 + 1.0 i \end{array}) .

10.1 Program Text

Program Text (f01flce.c)

10.2 Program Data

Program Data (f01flce.d)

10.3 Program Results

Program Results (f01flce.r)

NAG Library Manual, Mark 27.2

Interfaces: FL CL CPP AD

NAG CL Interface Introduction

F01 (Matop) Chapter Contents

F01 (Matop) Chapter Introduction

f01fl: FL CL CPP AD

© The Numerical Algorithms Group Ltd. 2021