[RBFOpt] Problem with 20d rosenbrock function

[Giacomo Nannicini]

Hi Andreas,
can you post the log to understand where and how it gets stuck?
Regarding the number of parallel evaluations: that's because in this
example the function evaluates almost instantaneously, so the library
never has the time to fill all the CPUs . In any case, it is unlikely
that it will be able to exploit 20 CPUs to good use: it does
pre-emptive evaluation of the points that are likely to be evaluated
in the future, but I am not sure it will be able to predict correctly
20 points in the future.

G
On Tue, Nov 20, 2018 at 5:29 AM Sattler, Dr. Andreas
<Andreas.Sattler at siltronic.com> wrote:
>
> Dear all,
>
>
>
> I was very happy with the performance of RBFopt when testing it on the 2D rosenbrock function, the number of function evaluations was very competitive! (150-300 evaluations)
>
>
>
> Then I went on to test it on a 20D rosenbrock function (because for production use I need to optimize in similar dimensions):
>
>
>
> n = self.dimension
>
> value=0;
>
> for i in range(0,19):
>
>    value += 100 * (x[i]*x[i] -x[i+1])*(x[i]*x[i] -x[i+1]) + (x[i]-1)*(x[i]-1);
>
>
>
> I ran it using these options:
>
>
>
> rbfopt_cl_interface.py --max_iterations=150000  --max_evaluations=150000 --max_cycles=100000 --global_search_method=sampling --num_cpus=20 --algorithm=MSRSM --eps_opt=1e-3 --num_global_searches=5 --target_objval=0 --log=example2.log  rbfopt_black_box_example2.py
>
>
>
> Unfortunately, the optimization got kinda stuck after a few thousand iterations.
>
> Also, there are very few parallel function evaluations left (about 2), slowing down the progress further.
>
>
>
> Has anyone successfully run RBFopt on this or a similar test problem?
>
> Maybe I am doing something wrong? Any ideas?
>
>
>
> Best regards,
>
> Andreas
>
>
>
>
>
>
>
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