f01ed: FL CL CPP AD

NAG FL Interface
f01edf (real_symm_matrix_exp)

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NAG Library Manual, Mark 27.1

Interfaces: FL CL CPP AD

NAG FL Interface Introduction

F01 (Matop) Chapter Contents

F01 (Matop) Chapter Introduction

f01ed: FL CL CPP AD

▸▿ 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

1 Purpose

f01edf computes the matrix exponential,

e^{A}

, of a real symmetric

n \times n

matrix

A

2 Specification

Fortran Interface

Subroutine f01edf (

uplo, n, a, lda, ifail)

Integer, Intent (In)	::	n, lda
Integer, Intent (Inout)	::	ifail
Real (Kind=nag_wp), Intent (Inout)	::	a(lda,*)
Character (1), Intent (In)	::	uplo

C Header Interface

#include <nag.h>

void	f01edf_ (const char uplo, const Integer n, double a[], const Integer lda, Integer ifail, const Charlen length_uplo)

The routine may be called by the names f01edf or nagf_matop_real_symm_matrix_exp.

3 Description

e^{A}

is computed using a spectral factorization of

A

A = Q D Q^{T},

where

D

is the diagonal matrix whose diagonal elements,

d_{i}

, are the eigenvalues of

A

, and

Q

is an orthogonal matrix whose columns are the eigenvectors of

A

e^{A}

is then given by

e^{A} = Q e^{D} Q^{T},

where

e^{D}

is the diagonal matrix whose

i

th diagonal element is

e^{d_{i}}

. See for example Section 4.5 of Higham (2008).

4 References

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

5 Arguments

1: $uplo$ – Character(1) Input

On entry: indicates whether the upper or lower triangular part of

A

is stored.

$uplo ='U'$: The upper triangular part of $A$ is stored.
$uplo ='L'$: The lower triangular part of $A$ is stored.

Constraint:

uplo ='U'

'L'

2: $n$ – Integer Input

On entry:

n

, the order of the matrix

A

Constraint:

n \geq 0

3: $a (lda, *)$ – Real (Kind=nag_wp) array Input/Output

Note: the second dimension of the array a must be at least

n

On entry: the

n \times n

symmetric matrix

A

If $uplo ='U'$ , the upper triangular part of $A$ must be stored and the elements of the array below the diagonal are not referenced.
If $uplo ='L'$ , the lower triangular part of $A$ must be stored and the elements of the array above the diagonal are not referenced.

On exit: if

ifail = 0

, the upper or lower triangular part of the

n \times n

matrix exponential,

e^{A}

4: $lda$ – Integer Input

On entry: the first dimension of the array a as declared in the (sub)program from which f01edf is called.

Constraint:

lda \geq \max (1, n)

5: $ifail$ – Integer Input/Output

On entry: ifail must be set to

0

- 1

1

to set behaviour on detection of an error; these values have no effect when no error is detected.

A value of

0

causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of

- 1

means that an error message is printed while a value of

1

means that it is not.

If halting is not appropriate, the value

- 1

1

is recommended. If message printing is undesirable, then the value

1

is recommended. Otherwise, the value

0

is recommended. When the value $- 1$ or $1$ is used it is essential to test the value of ifail on exit.

On exit:

ifail = 0

unless the routine detects an error or a warning has been flagged (see Section 6).

6 Error Indicators and Warnings

If on entry

ifail = 0

- 1

, explanatory error messages are output on the current error message unit (as defined by x04aaf).

Errors or warnings detected by the routine:

$ifail > 0$: The computation of the spectral factorization failed to converge.
The value of ifail gives the number of off-diagonal elements of an intermediate tridiagonal form that did not converge to zero (see f08faf).

$ifail = - 1$: On entry, $uplo = ⟨ value ⟩$ .
Constraint: $uplo ='L'$ or $'U'$ .

$ifail = - 2$: On entry, $n = ⟨ value ⟩$ .
Constraint: $n \geq 0$ .

$ifail = - 3$: An internal error occurred when computing the spectral factorization. Please contact NAG.

$ifail = - 4$: On entry, $lda = ⟨ value ⟩$ and $n = ⟨ value ⟩$ .
Constraint: $lda \geq n$ .

$ifail = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.
See Section 7 in the Introduction to the NAG Library FL Interface for further information.

$ifail = - 399$: Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library FL Interface for further information.

$ifail = - 999$: Dynamic memory allocation failed.
See Section 9 in the Introduction to the NAG Library FL Interface for further information.

7 Accuracy

For a symmetric matrix

A

, the matrix

e^{A}

, has the relative condition number

κ (A) = {‖ A ‖}_{2},

which is the minimum possible for the matrix exponential and so the computed matrix exponential is guaranteed to be close to the exact matrix. See Section 10.2 of Higham (2008) for details and further discussion.

8 Parallelism and Performance

f01edf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

f01edf 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 routine. 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 the real allocatable memory required is approximately

(n + nb + 4) \times n

, where nb is the block size required by f08faf.

The cost of the algorithm is

O (n^{3})

As well as the excellent book cited above, the classic reference for the computation of the matrix exponential is Moler and Van Loan (2003).

10 Example

This example finds the matrix exponential of the symmetric matrix

A = (\begin{matrix} 1 & 2 & 3 & 4 \\ 2 & 1 & 2 & 3 \\ 3 & 2 & 1 & 2 \\ 4 & 3 & 2 & 1 \end{matrix})

f01ed: FL CL CPP AD

NAG FL Interfacef01edf (real_​symm_​matrix_​exp)

▸▿ 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 FL Interface
f01edf (real_symm_matrix_exp)