[RBFOpt] Problem with 20d rosenbrock function

Sattler, Dr. Andreas Andreas.Sattler at siltronic.com
Wed Nov 21 03:08:47 EST 2018


Hi Giacomo,

some new "insight" from my side: I started another optimization run with added "sleep" statements in the objective function -- for 10 seconds.
This time it was doing 20 parallel evaluations all the time, mostly global search steps, and did not get "stuck" in the previous way.
This confirms what you wrote about parallel evaluations.

Unfortunately both runs don't get any lower in objective function than about 15 (with 0 being the target).
I am now at 60 000 iterations. For a similar budget even a more expensive algorithm (in terms of function evaluations) than RBFopt like differential evolution finds the solution.

In conclusion one probably has to consider this problem too difficult for the current RBFopt?!

Best regards,
Andreas
--
P.S.: I hope you got my message from yesterday: there was a warning that it needed approval from the forum moderator due to it exceeding some size limit.

-----Original Message-----
From: Giacomo Nannicini <giacomo.n at gmail.com> 
Sent: Tuesday, November 20, 2018 12:42 PM
To: Sattler, Dr. Andreas <Andreas.Sattler at siltronic.com>
Cc: rbfopt at list.coin-or.org
Subject: Re: [RBFOpt] Problem with 20d rosenbrock function

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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