[SciPy-dev] Advice on Simulated Annealing (ticket #875)

josef.pktd@gmai... josef.pktd@gmai...
Tue Mar 3 08:20:42 CST 2009

On Tue, Mar 3, 2009 at 7:45 AM, Adolph J. Vogel <ajvogel@tuks.co.za> wrote:
> Josef
>>I checked again and neither anneal nor brute have any tests. Are there
>>any good or classical test cases for global optimization?
> I have "A Collection of Test Problems for Constrained Global Optimization
> Algorithms" by C.A Floudas on my desk at the moment. Maybe they can be used
> for tests for the algorithms.
> If you`d like I can type up some of the problems and relay them to list?
> Regards Adolph
> --
> Adolph J. Vogel BEng(Mech)

Yes, this would be very useful, both for quality control in scipy and
making refactoring more reliable.

With nose testing it is very easy to convert an example to a test if
you know the correct answer:

from numpy.testing import assert_array_almost_equal     # or similar
....(calculate result)
assert_array_almost_equal(result, expected_result, decimal)

For examples and tests, a few easy and a few "tough" problems would be
enough. It would be good if some of the examples can also be solved by
unconstrained optimizers or if the constraints can be included through
transformation of the parameters to test the current implementation.

It would also be very helpful to report any problems or limitations
with the current code that your examples and tests might uncover,
either on the mailing list or by opening a ticket.

Thank you,


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