[RBFOpt] Problem with parallelization

[Cristina Elsido]

Hi,
First of all thank you for your work and for sharing the Rbfopt library.
I have recently downloaded Rbfopt (v4.1.1). I would like to use Rbfopt for the optimization of an expensive black-box function (about 10' per evaluation), but I am experiencing some problems with parallelization. I noticed that if I run an optimization problem with the parameter "num_cpus" larger than 1, the optimization gets 'stuck', showing no output or progress even for many hours.

I have also tested the parallel version of the optimizer with the minimum working example, and I got the same problem (instead, the serial version with the same parameters except for the num_cpus was solved in few seconds). Here is the script I used:

import rbfopt
import numpy as np

def obj_funct(x):
  return x[0]*x[1] - x[2]

bb = rbfopt.RbfoptUserBlackBox(3, np.array([0] * 3), np.array([10] * 3),
                               np.array(['R', 'I', 'R']), obj_funct)
settings = rbfopt.RbfoptSettings(max_evaluations=50,num_cpus=4,print_solver_output=False,algorithm='Gutmann')
alg = rbfopt.RbfoptAlgorithm(settings, bb)
val, x, itercount, evalcount, fast_evalcount = alg.optimize()

Should I use different settings for parallelization? Which parameters do you recommend for the parallel optimizer?
Thanks in advance for your help.
Best regards,
Cristina Elsido, PhD
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