NAG Library Function Document

nag_matop_real_gen_matrix_cond_usd (f01jcc)


    1  Purpose
    7  Accuracy


nag_matop_real_gen_matrix_cond_usd (f01jcc) computes an estimate of the absolute condition number of a matrix function f at a real n by n matrix A in the 1-norm, using analytical derivatives of f you have supplied.


#include <nag.h>
#include <nagf01.h>
void  nag_matop_real_gen_matrix_cond_usd (Integer n, double a[], Integer pda,
void (*f)(Integer m, Integer *iflag, Integer nz, const Complex z[], Complex fz[], Nag_Comm *comm),
Nag_Comm *comm, Integer *iflag, double *conda, double *norma, double *normfa, NagError *fail)


The absolute condition number of f at A, condabsf,A is given by the norm of the Fréchet derivative of f, LA, which is defined by
LX := maxE0 LX,E E ,  
where LX,E is the Fréchet derivative in the direction E. LX,E is linear in E and can therefore be written as
vec LX,E = KX vecE ,  
where the vec operator stacks the columns of a matrix into one vector, so that KX is n2×n2. nag_matop_real_gen_matrix_cond_usd (f01jcc) computes an estimate γ such that γ KX 1 , where KX 1 n-1 LX 1 , n LX 1 . The relative condition number can then be computed via
cond rel f,A = cond abs f,A A1 fA 1 .  
The algorithm used to find γ is detailed in Section 3.4 of Higham (2008).
The function f, and the derivatives of f, are returned by function f which, given an integer m, evaluates fmzi at a number of (generally complex) points zi, for i=1,2,,nz. For any z on the real line, fz must also be real. nag_matop_real_gen_matrix_cond_usd (f01jcc) is therefore appropriate for functions that can be evaluated on the complex plane and whose derivatives, of arbitrary order, can also be evaluated on the complex plane.


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


1:     n IntegerInput
On entry: n, the order of the matrix A.
Constraint: n0.
2:     a[dim] doubleInput/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, fA.
3:     pda IntegerInput
On entry: the stride separating matrix row elements in the array a.
Constraint: pdan.
4:     f function, supplied by the userExternal Function
Given an integer m, the function f evaluates fmzi at a number of points zi.
The specification of f is:
void  f (Integer m, Integer *iflag, Integer nz, const Complex z[], Complex fz[], Nag_Comm *comm)
1:     m IntegerInput
On entry: the order, m, of the derivative required.
If m=0, fzi should be returned. For m>0, fmzi should be returned.
2:     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 fz; for instance fz may not be defined. If iflag is returned as nonzero then nag_matop_real_gen_matrix_cond_usd (f01jcc) will terminate the computation, with fail.code= NE_USER_STOP.
3:     nz IntegerInput
On entry: nz, the number of function or derivative values required.
4:     z[nz] const ComplexInput
On entry: the nz points z1,z2,,znz at which the function f is to be evaluated.
5:     fz[nz] ComplexOutput
On exit: the nz function or derivative values. fz[i-1] should return the value fmzi, for i=1,2,,nz. If zi lies on the real line, then so must fmzi.
6:     comm Nag_Comm *
Pointer to structure of type Nag_Comm; the following members are relevant to f.
userdouble *
iuserInteger *
The type Pointer will be void *. Before calling nag_matop_real_gen_matrix_cond_usd (f01jcc) you may allocate memory and initialize these pointers with various quantities for use by f when called from nag_matop_real_gen_matrix_cond_usd (f01jcc) (see Section in How to Use the NAG Library and its Documentation).
Note: f should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by nag_matop_real_gen_matrix_cond_usd (f01jcc). If your code inadvertently does return any NaNs or infinities, nag_matop_real_gen_matrix_cond_usd (f01jcc) is likely to produce unexpected results.
5:     comm Nag_Comm *
The NAG communication argument (see Section in How to Use the NAG Library and its Documentation).
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_USER_STOP.
7:     conda double *Output
On exit: an estimate of the absolute condition number of f at A.
8:     norma double *Output
On exit: the 1-norm of A.
9:     normfa double *Output
On exit: the 1-norm of fA.
10:   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.
An internal error occurred when estimating the norm of the Fréchet derivative of f at A. Please contact NAG.
An internal error occurred when evaluating the matrix function fA. You can investigate further by calling nag_matop_real_gen_matrix_fun_usd (f01emc) with the matrix A and the function f.
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.
iflag has been set nonzero by the user-supplied function.


nag_matop_real_gen_matrix_cond_usd (f01jcc) uses the norm estimation routine nag_linsys_real_gen_norm_rcomm (f04ydc) to estimate a quantity γ, where γ KX 1  and KX 1 n-1 LX 1 , n LX 1 . For further details on the accuracy of norm estimation, see the documentation for nag_linsys_real_gen_norm_rcomm (f04ydc).

Parallelism and Performance

nag_matop_real_gen_matrix_cond_usd (f01jcc) 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.
nag_matop_real_gen_matrix_cond_usd (f01jcc) 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

The matrix function is computed using the underlying matrix function routine nag_matop_real_gen_matrix_fun_usd (f01emc). Approximately 6n2 of real allocatable memory is required by the routine, in addition to the memory used by the underlying matrix function routine.
If only fA is required, without an estimate of the condition number, then it is far more efficient to use the underlying matrix function routine directly.
The complex analogue of this function is nag_matop_complex_gen_matrix_cond_usd (f01kcc).


This example estimates the absolute and relative condition numbers of the matrix function e2A where
A= 0 -1 -1 1 -2 0 1 -1 2 -1 2 -2 -1 -2 0 -1 .  

Program Text

Program Text (f01jcce.c)

Program Data

Program Data (f01jcce.d)

Program Results

Program Results (f01jcce.r)

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