Comments (5)
Hi!
- That is correct. Are you interested in using another stopping criterion? I would be happy the add some and doing this shouldn't require much work on my part.
- This example should be helpful: https://github.com/dme65/pySOT/blob/master/pySOT/test/test_subprocess.py
pySOT will try to go back and evaluate this input again if something went wrong the first time.
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Thanks for information and for the example.
Regarding to stopping criterion: especially during testing stage (which means before the final start of (possibly) very time consuming optimization process) I'd welcome "real time" criterion - something like run till Monday morning ;). And sometimes it'd be useful to have criterion related to fitness value (absolute value or no improvement after specified number of evaluations for example).
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Hi,
+1 to a stopping criteria.
Outstanding work by the way.
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@dbindel: Is there support for this in POAP?
@SanPen: I'm glad you like pySOT!
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A real-time stopping criterion will be added to the next version.
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Related Issues (20)
- Implement SO-MI
- MPI example gets stuck HOT 3
- PendingDeprecationWarning from use of NumPy matrices HOT 2
- how to surrogate model with multiple variable? HOT 5
- Multiple threads does not work HOT 10
- Evaluates the uncertainty of the surrogate model at points x HOT 3
- pySOT may propose the same point twice on pure integer problems HOT 1
- Add back option to restart algorithms when they converge early HOT 1
- mpiexample_simple.py fails after the increase of number of workers HOT 3
- Documentation has an error HOT 1
- `examples` and `tests` are being installed as top-level packages (directly in site-packages) HOT 5
- SurrogateUnitBox not longer required for RBF? HOT 1
- comparisons HOT 1
- MIssing parentheses
- Use of "extra" and "extra_vals"
- add_points() does not handle objective functions that output integers
- Comments in ei_merit() do not match the actual sizes of parameters passed to the function
- In gp.py, predict() returns incorrectly-sized array, causing IndexError exception in ei_merit().
- pySOT is slow to generate new points with large dataset HOT 1
- Stopping criteria based on objective and/or decision variable tolerance
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