Comments (1)
There is currently no support for computationally expensive constraints, and as you saw in my previous reply, all constraint handling was removed in 0.2.0. There is clearly support from POAP to pass the values from computationally expensive constraints back to the strategy, but we have no strategy that can handle this.
The standard approach to computationally expensive black-box constraints is to use surrogate models to approximate the constraints. Such a strategy will fit well with the new framework, but I'll need help with the implementation since I just graduated.
We definitely need support for analytical and black-box constraint handling.
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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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