NAG Library Routine Document

f01jdf (real_gen_matrix_cond_sqrt)


    1  Purpose
    7  Accuracy


f01jdf computes an estimate of the relative condition number κA1/2 and a bound on the relative residual, in the Frobenius norm, for the square root of a real n by n matrix A. The principal square root, A1/2, of A is also returned.


Fortran Interface
Subroutine f01jdf ( n, a, lda, alpha, condsa, ifail)
Integer, Intent (In):: n, lda
Integer, Intent (Inout):: ifail
Real (Kind=nag_wp), Intent (Inout):: a(lda,*)
Real (Kind=nag_wp), Intent (Out):: alpha, condsa
C Header Interface
#include nagmk26.h
void  f01jdf_ (const Integer *n, double a[], const Integer *lda, double *alpha, double *condsa, Integer *ifail)


For a matrix with no eigenvalues on the closed negative real line, the principal matrix square root, A1/2, of A is the unique square root with eigenvalues in the right half-plane.
The Fréchet derivative of a matrix function A1/2 in the direction of the matrix E is the linear function mapping E to LA,E such that
A+E1/2 - A1/2 - LA,E = oA .  
The absolute condition number is given by the norm of the Fréchet derivative which is defined by
LA := maxE0 LA,E E .  
The Fréchet derivative is linear in E and can therefore be written as
vec LA,E = KA vecE ,  
where the vec operator stacks the columns of a matrix into one vector, so that KA is n2×n2.
f01jdf uses Algorithm 3.20 from Higham (2008) to compute an estimate γ such that γ KX F . The quantity of γ provides a good approximation to LAF. The relative condition number, κA1/2, is then computed via
κA1/2 = LAF AF A1/2 F .  
κA1/2 is returned in the argument condsa.
A1/2 is computed using the algorithm described in Higham (1987). This is a real arithmetic version of the algorithm of Björck and Hammarling (1983). In addition, a blocking scheme described in Deadman et al. (2013) is used.
The computed quantity α is a measure of the stability of the relative residual (see Section 7). It is computed via
α= A 1/2 F 2 AF .  


Björck Å and Hammarling S (1983) A Schur method for the square root of a matrix Linear Algebra Appl. 52/53 127–140
Deadman E, Higham N J and Ralha R (2013) Blocked Schur Algorithms for Computing the Matrix Square Root Applied Parallel and Scientific Computing: 11th International Conference, (PARA 2012, Helsinki, Finland) P. Manninen and P. Öster, Eds Lecture Notes in Computer Science 7782 171–181 Springer–Verlag
Higham N J (1987) Computing real square roots of a real matrix Linear Algebra Appl. 88/89 405–430
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:     alda* – Real (Kind=nag_wp) arrayInput/Output
Note: the second dimension of the array a must be at least n.
On entry: the n by n matrix A.
On exit: contains, if ifail=0, the n by n principal matrix square root, A1/2. Alternatively, if ifail=1, contains an n by n non-principal square root of A.
3:     lda – IntegerInput
On entry: the first dimension of the array a as declared in the (sub)program from which f01jdf is called.
Constraint: ldan.
4:     alpha – Real (Kind=nag_wp)Output
On exit: an estimate of the stability of the relative residual for the computed principal (if ifail=0) or non-principal (if ifail=1) matrix square root, α.
5:     condsa – Real (Kind=nag_wp)Output
On exit: an estimate of the relative condition number, in the Frobenius norm, of the principal (if ifail=0) or non-principal (if ifail=1) matrix square root at A, κA1/2.
6:     ifail – IntegerInput/Output
On entry: ifail must be set to 0, -1​ or ​1. If you are unfamiliar with this argument you should refer to Section 3.4 in How to Use the NAG Library and its Documentation for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value -1​ 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 argument, the recommended value is 0. 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).

Error Indicators and Warnings

If on entry ifail=0 or -1, explanatory error messages are output on the current error message unit (as defined by x04aaf).
Errors or warnings detected by the routine:
A has a semisimple vanishing eigenvalue. A non-principal square root was returned.
A has a defective vanishing eigenvalue. The square root and condition number cannot be found in this case.
A has a negative real eigenvalue. The principal square root is not defined. f01kdf can be used to return a complex, non-principal square root.
An error occurred when computing the matrix square root. Consequently, alpha and condsa could not be computed. It is likely that the routine was called incorrectly.
An error occurred when computing the condition number. The matrix square root was still returned but you should use f01enf to check if it is the principal matrix square root.
On entry, n=value.
Constraint: n0.
On entry, lda=value and n=value.
Constraint: ldan.
An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.9 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 3.8 in How to Use the NAG Library and its Documentation for further information.
Dynamic memory allocation failed.
See Section 3.7 in How to Use the NAG Library and its Documentation for further information.


If the computed square root is X~, then the relative residual
A - X~2 F AF ,  
is bounded approximately by nαε, where ε is machine precision. The relative error in X~ is bounded approximately by nακA1/2ε.

Parallelism and Performance

f01jdf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f01jdf 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.

Further Comments

Approximately 3×n2 of real allocatable memory is required by the routine.
The cost of computing the matrix square root is 85n3/3 floating-point operations. The cost of computing the condition number depends on how fast the algorithm converges. It typically takes over twice as long as computing the matrix square root.
If condition estimates are not required then it is more efficient to use f01enf to obtain the matrix square root alone. Condition estimates for the square root of a complex matrix can be obtained via f01kdf.


This example estimates the matrix square root and condition number of the matrix
A = -5 2 -1 1 -2 -3 19 27 -9 0 15 24 7 8 11 16 .  

Program Text

Program Text (f01jdfe.f90)

Program Data

Program Data (f01jdfe.d)

Program Results

Program Results (f01jdfe.r)

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