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Hi,</div>
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First of all thank you for your work and for sharing the Rbfopt library.</div>
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I have recently downloaded Rbfopt (v4.1.1). I would like to use Rbfopt for the optimization of an expensive black-box function
<span style="font-family: Calibri, Arial, Helvetica, sans-serif; background-color: rgb(255, 255, 255); display: inline !important">
(about 10' per evaluation)</span>, 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.</div>
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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:</div>
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<span>import rbfopt</span></div>
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<div>import numpy as np</div>
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<div>def obj_funct(x):</div>
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<div> return x[0]*x[1] - x[2]</div>
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<div>bb = rbfopt.RbfoptUserBlackBox(3, np.array([0] * 3), np.array([10] * 3),</div>
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<div> np.array(['R', 'I', 'R']), obj_funct)</div>
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<div>settings = rbfopt.RbfoptSettings(max_evaluations=50,num_cpus=4,print_solver_output=False,algorithm='Gutmann')</div>
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<div>alg = rbfopt.RbfoptAlgorithm(settings, bb)</div>
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<span>val, x, itercount, evalcount, fast_evalcount = alg.optimize()</span></div>
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<span>Should I use different settings for parallelization? Which parameters do you recommend for the parallel optimizer?</span></div>
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<span>Thanks in advance for your help.</span></div>
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<span>Best regards,</span></div>
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<span>Cristina Elsido, PhD</span></div>
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