CODEWITHSUNDEEP
openmdao
Skyrider
Skyrider

Reputation: 133

pyOptSparse Error: Received an unknown option (AMIEGO)

I recently came across AMIEGO. When I try to run the example problems (provided in the example directory) I get the following error.

-------------------------------------------------------------------------------

Exit Flag: True
Elapsed Time: 0.04829263687133789
======================ContinuousOptimization-End=======================================

+------------------------------------------------------------------------------+
| pyOptSparse Error: Received an unknown option: 'Major optimality tolerance'  |
+------------------------------------------------------------------------------+

Traceback (most recent call last):
  File "/home/sky/anaconda3/lib/python3.8/site-packages/openmdao/utils/concurrent.py", line 65, in concurrent_eval_lb
    retval = func(*args)
  File "/home/sky/anaconda3/lib/python3.8/site-packages/amiego/kriging.py", line 239, in _calculate_thetas
    opt_x, opt_f, success, msg = snopt_opt(_calcll, x0, low, high, title='kriging',
  File "/home/sky/anaconda3/lib/python3.8/site-packages/amiego/optimize_function.py", line 76, in snopt_opt
    opt.setOption(name, value)
  File "/home/sky/anaconda3/lib/python3.8/site-packages/pyoptsparse/pyOpt_optimizer.py", line 829, in setOption
    raise Error("Received an unknown option: %s" % repr(name))
pyoptsparse.pyOpt_error.Error

I tested pyoptsparse optimization driver with the sellar problem and it worked as expected. So I think I'm missing something in AMIEGO. And fyi I didn't modify anything in the example, so I am running it with SLSQP(from pyoptsparse driver) for the continuous part(I dont have SNOPT). Any pointers on how to fix this or where to start looking will be helpful.

Upvotes: 0

Views: 76

Answers (1)

Kenneth Moore
Kenneth Moore

Reputation: 2202

I've pushed up a couple of fixes to the repository so that you can run it without SNOPT. The basic Branin problem in the examples works and gets to the expected answer now. I can't promise that SLSQP is the best choice for more complicated problems as we usually favor SNOPT over SLSQP in our work. This is still very experimental code, so the documentation is weak and there are still a lot of control knobs and flags that are buried as subcomponent attributes (including ideas that we tried that didn't pan out). But we appreciate users who are willing to try AMIEGO and help us improve it.

Upvotes: 1

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