nag_zhpcon (f07puc) estimates the condition number of a complex Hermitian indefinite matrix
, where
has been factorized by
nag_zhptrf (f07prc), using packed storage.
nag_zhpcon (f07puc) estimates the condition number (in the
-norm) of a complex Hermitian indefinite matrix
:
Since
is Hermitian,
.
The function should be preceded by a call to
nag_zhp_norm (f16udc) to compute
and a call to
nag_zhptrf (f07prc) to compute the Bunch–Kaufman factorization of
. The function then uses Higham's implementation of Hager's method (see
Higham (1988)) to estimate
.
Higham N J (1988) FORTRAN codes for estimating the one-norm of a real or complex matrix, with applications to condition estimation ACM Trans. Math. Software 14 381–396
The computed estimate
rcond is never less than the true value
, and in practice is nearly always less than
, although examples can be constructed where
rcond is much larger.
nag_zhpcon (f07puc) 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.
A call to nag_zhpcon (f07puc) involves solving a number of systems of linear equations of the form
; the number is usually
and never more than
. Each solution involves approximately
real floating-point operations but takes considerably longer than a call to
nag_zhptrs (f07psc) with one right-hand side, because extra care is taken to avoid overflow when
is approximately singular.
The real analogue of this function is
nag_dspcon (f07pgc).
This example estimates the condition number in the
-norm (or
-norm) of the matrix
, where
Here
is Hermitian indefinite, stored in packed form, and must first be factorized by
nag_zhptrf (f07prc). The true condition number in the
-norm is
.