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View Code? Open in Web Editor NEW๐ฏ A comprehensive gradient-free optimization framework written in Python
Home Page: https://100.github.io/Solid/
License: MIT License
๐ฏ A comprehensive gradient-free optimization framework written in Python
Home Page: https://100.github.io/Solid/
License: MIT License
It would be good to find all of the instances where the algorithms may be unstable and handle these cases appropriately (such as overflow). Some cases are handled, but there are probably more.
Currently, the testing just makes sure that the algorithm runs without error on a toy problem.
It would be nice to do something more akin to unit testing, but I'm not quite sure how to do it in this situation since a lot of the testable functionality is provided by the user.
Of course, more algorithms are always great.
Some suggestions:
Where applicable, it would be nice to implement both the continuous and discrete versions of algorithms.
The run()
method of the SimulatedAnnealing class has a bug when the annealing method does not find a better state than the initial one.
The bug happens when the annealing algorithm fails to find a better state than the initial one. This can happen when the maximum number of steps is low or when the initial guess is already very good.
The tuple returned by the run()
method is (None, cost_of_initial_state)
.
Add the line
self.best_state = deepcopy(self.current_state)
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