NAG Library Function Document

nag_matop_complex_gen_matrix_cond_exp (f01kgc)


    1  Purpose
    7  Accuracy


nag_matop_complex_gen_matrix_cond_exp (f01kgc) computes an estimate of the relative condition number κexpA of the exponential of a complex n by n matrix A, in the 1-norm. The matrix exponential eA is also returned.


#include <nag.h>
#include <nagf01.h>
void  nag_matop_complex_gen_matrix_cond_exp (Integer n, Complex a[], Integer pda, double *condea, NagError *fail)


The Fréchet derivative of the matrix exponential of A is the unique linear mapping ELA,E such that for any matrix E 
eA+E - e A - LA,E = oE .  
The derivative describes the first-order effect of perturbations in A on the exponential eA.
The relative condition number of the matrix exponential can be defined by
κexpA = LA A expA ,  
where LA is the norm of the Fréchet derivative of the matrix exponential at A.
To obtain the estimate of κexpA, nag_matop_complex_gen_matrix_cond_exp (f01kgc) first estimates LA by computing an estimate γ of a quantity Kn-1LA1,nLA1, such that γK.
The algorithms used to compute κexpA are detailed in the Al–Mohy and Higham (2009a) and Al–Mohy and Higham (2009b).
The matrix exponential eA is computed using a Padé approximant and the scaling and squaring method. The Padé approximant is differentiated to obtain the Fréchet derivatives LA,E which are used to estimate the condition number.


Al–Mohy A H and Higham N J (2009a) A new scaling and squaring algorithm for the matrix exponential SIAM J. Matrix Anal. 31(3) 970–989
Al–Mohy A H and Higham N J (2009b) Computing the Fréchet derivative of the matrix exponential, with an application to condition number estimation SIAM J. Matrix Anal. Appl. 30(4) 1639–1657
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


1:     n IntegerInput
On entry: n, the order of the matrix A.
Constraint: n0.
2:     a[dim] ComplexInput/Output
Note: the dimension, dim, of the array a must be at least pda×n.
The i,jth element of the matrix A is stored in a[j-1×pda+i-1].
On entry: the n by n matrix A.
On exit: the n by n matrix exponential eA.
3:     pda IntegerInput
On entry: the stride separating matrix row elements in the array a.
Constraint: pdan.
4:     condea double *Output
On exit: an estimate of the relative condition number of the matrix exponential κexpA.
5:     fail NagError *Input/Output
The NAG error argument (see Section 3.7 in How to Use the NAG Library and its Documentation).

Error Indicators and Warnings

Dynamic memory allocation failed.
See Section in How to Use the NAG Library and its Documentation for further information.
On entry, argument value had an illegal value.
On entry, n=value.
Constraint: n0.
On entry, pda=value and n=value.
Constraint: pdan.
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 2.7.6 in How to Use the NAG Library and its Documentation for further information.
Your licence key may have expired or may not have been installed correctly.
See Section 2.7.5 in How to Use the NAG Library and its Documentation for further information.
The linear equations to be solved for the Padé approximant are singular; it is likely that this function has been called incorrectly.
eA has been computed using an IEEE double precision Padé approximant, although the arithmetic precision is higher than IEEE double precision.


nag_matop_complex_gen_matrix_cond_exp (f01kgc) uses the norm estimation function nag_linsys_complex_gen_norm_rcomm (f04zdc) to produce an estimate γ of a quantity Kn-1LA1,nLA1, such that γK. For further details on the accuracy of norm estimation, see the documentation for nag_linsys_complex_gen_norm_rcomm (f04zdc).
For a normal matrix A (for which AHA=AAH) the computed matrix, eA, is guaranteed to be close to the exact matrix, that is, the method is forward stable. No such guarantee can be given for non-normal matrices. See Section 10.3 of Higham (2008) for details and further discussion.
For further discussion of the condition of the matrix exponential see Section 10.2 of Higham (2008).

Parallelism and Performance

nag_matop_complex_gen_matrix_cond_exp (f01kgc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
nag_matop_complex_gen_matrix_cond_exp (f01kgc) 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.

Further Comments

nag_matop_complex_gen_matrix_cond_std (f01kac) uses a similar algorithm to nag_matop_complex_gen_matrix_cond_exp (f01kgc) to compute an estimate of the absolute condition number (which is related to the relative condition number by a factor of A/expA). However, the required Fréchet derivatives are computed in a more efficient and stable manner by nag_matop_complex_gen_matrix_cond_exp (f01kgc) and so its use is recommended over nag_matop_complex_gen_matrix_cond_std (f01kac).
The cost of the algorithm is On3 and the complex allocatable memory required is approximately 15n2; see Al–Mohy and Higham (2009a) and Al–Mohy and Higham (2009b) for further details.
If the matrix exponential alone is required, without an estimate of the condition number, then nag_matop_complex_gen_matrix_exp (f01fcc) should be used. If the Fréchet derivative of the matrix exponential is required then nag_matop_complex_gen_matrix_frcht_exp (f01khc) should be used.
As well as the excellent book Higham (2008), the classic reference for the computation of the matrix exponential is Moler and Van Loan (2003).


This example estimates the relative condition number of the matrix exponential eA, where
A = 1+0i 2+0i 2+0i 2+i 3+2i 1i+0 1i+0 2+i 3+2i 2+0i 1i+0 2+i 3+2i 3+2i 3+2i 1+i .  

Program Text

Program Text (f01kgce.c)

Program Data

Program Data (f01kgce.d)

Program Results

Program Results (f01kgce.r)

© The Numerical Algorithms Group Ltd, Oxford, UK. 2017