The above was forwarded to the Scipy list, but I guess enough people
here care:
-------- Original Message --------
Date: Wed, 19 Nov 2008 06:03:40 -0800
From: James Demmel <[EMAIL PROTECTED]>
To: Kreinovich, Vladik <[EMAIL PROTECTED]>, reliable
computing
<[EMAIL PROTECTED]>
CC: James Demmel <[EMAIL PROTECTED]>
Subject: Announcement for Reliable Computing
I would appreciate it if you could post this on your
mailing list.
Thanks,
Jim Demmel
Subject: New LAPACK release with "guaranteed" error bounds
We have just released LAPACK 3.2, which among other
improvements
includes iterative refinement for solving linear systems
with
"guaranteed" error bounds, measured both normwise and
componentwise.
Portable high precision arithmetic (using another package
we just
released called XBLAS) is used to compute residuals. What
we mean
by "guarantee" is that either the error bound is correctly
O(machine_epsilon), or a warning is returned that the
condition number
is ~1/machine_epsilon or larger. In extensive testing, we
have never
found it
to fail, either by returning a too-small error bound, or
failing to solve
a problem whose condition number is at least a little
below
1/machine_epsilon.
But if there is a community that can find a failure, it is
this one, and we
would be very interested if you can find one. Since we are
not doing
interval arithmetic, we expect to keep "guaranteed" in
quotation marks.
For more information, please see
http://www.netlib.org/lapack/ and
http://www.netlib.org/xblas/
http://www.netlib.org/lapack/lapack-3.2.html
Regards,
Jim Demmel, and the rest of the LAPACK team
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