[RBFOpt] Problem with parallelization

[Giacomo Nannicini]

Hi Cristina,
could you be more specific about "getting stuck"? Can you post a log
of the parallel and serial version on your problem?
The minimal working example seems to work fine, even in parallel mode.
There are many "Discarded" iterations and a few restarts, but that's
because the algorithm finds the optimum after a few iterations and
afterward realizes that the model is not improving, hence the
restarts.

G

On Thu, Mar 12, 2020 at 1:42 PM Cristina Elsido
<cristina.elsido at polimi.it> wrote:
>
> 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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