On 10/20/06, Alexandar Hansen <viochemist@xxxxxxxxx> wrote:
First, I tried the test-suite again, and it failed (new failures this time
though :) )
#############################
# Results of the test suite #
#############################
Updated to revision 2654:
The model-free tests:
Constrained BFGS opt, backtracking line search {S2=0.970, te=2048,
Rex=0.149} ....... [ Failed ]
Constrained BFGS opt, backtracking line search {S2=0.970, te=2048,
Rex=0.149} ....... [ Failed ]
Constrained Newton opt, GMW Hessian mod, backtracking line search
{S2=0.970, te=2048, Rex=0.149} [ Failed ]
Constrained Newton opt, GMW Hessian mod, More and Thuente line search
{S2=0.970, te=2048, Rex=0.149 } [ Failed ]
I've deliberately started a new thread to talk about optimisation
tests in the test suite. This originates from the post located at
https://mail.gna.org/public/relax-devel/2006-10/msg00112.html
(Message-id: <481156b20610190725ud6bab67w1f8fbbdf849da52c@xxxxxxxxxxxxxx>).
These are new optimisation tests I have added. The problem with
setting up these types of test is that machine precision and round-off
error causes slightly different optimisation results on different
systems (different numbers of iterations, function counts, gradient
counts, etc). The model-free parameters should be, to within machine
precision, the same each time. Things which may influence this are
the CPU type, Python version, Numeric version, underlying C library,
operating system, etc.
Would you have the text output of one of these tests? Can you see if
it is a model-free parameter or optimisation static causing the
problem? I've tried the tests on Windows and exactly the same tests
fail (excluding the third one). The problem is the chi-squared value
is ~1e-24 when ~1e-27 was expected. Optimisation terminates a little
earlier on Windows (less iterations of the optimisation algorithm).
I'm wondering if testing the optimisation statistics is worthwhile in
the test suite considering the variability?
Edward
Optimisation tests in the test suite.