Here is the reason for the final evaluation:
+ The search for the design point should be done using an inequality constraint:
u_opt = argmin |u|^2 with f(u) >= s
+ In fact we assume that the constraint is active at the optimum, so the 
problem is replaced by:
u_opt = argmin |u|^2 with f(u) = s
+ The resulting approximation of the probability of the event is then 
Phi(-|u_opt|) if the origin of the standard space is NOT in the event (in which 
case both optimization problems give the same result), or Phi(|u_opt|) if the 
origin of the standard space is in the event (in which case the problems 
differ, the solution of the first one being 0 and only the second one gives 
useful information for the approximation)
-> so we have to check if the origin of the standard space is in the event or 
not, and this test is done AFTER the optimization step. If you have activated 
the cache mechanism of your function, this evaluation has no overhead most of 
the time, since the most usual starting point is precisely the origin of the 
standard space. Note that this point corresponds to the point with coordinates 
equal to the median of each marginal distribution, also equal to the mean eg 
for symmetrical marginal distributions.

If you are using the COBYLA solver, the error control and stopping criterion is 
delegated to the solver. The error history you get after the reconstruction is 
rebuilt as a post-processing of the optimization result and the history 
mechanism coupled to the objective and constraint functions. There is no 
guarantee at all that COBYLA used the exact same values to stop.

Another technical detail: the evaluations driven by COBYLA don't have the same 
goal. Some of them are there to substitute to a gradient a kind of linear 
approximation, and the convergence check is not done on these values, and the 
others are specifically there to monitor the convergence. It can explain the 
behavior you get.

Note that if you use other solvers (AbdoRackwitz or from NLOpt) you will have 
another behavior for the convergence management, but you will ALWAYS have a 
final evaluation at the origin of the standard space.

Cheers

Régis

________________________________
De : Anita Laera <[email protected]>
À : Julien Schueller <[email protected]> 
Cc : users <[email protected]>
Envoyé le : Mercredi 15 mars 2017 13h04
Objet : Re: [ot-users] Evaluations vs. iterations - FORM



Hi Julien,
thank you. I understand the reason, especially in the case of a gradient-based 
optimization algorithm
I am now using Cobyla and I would like to understand why I reach convergence at 
the iteration 65 (at least counting the number of rows in the error history), 
which corresponds to the results of the evaluation 65, but, after that, I have 
other 7 evaluations and the last one is a calculation with all mean parameters 
(i.e. my starting point).
Thank you!


2017-03-13 17:15 GMT+01:00 Julien Schueller <[email protected]>:

Hello,
>
>
>
>Short answer: the evaluations per iteration depend on the solver used and the 
>dimension.
>
>See http://trac.openturns.org/ ticket/351
>
>
>j
>
>
>
>________________________________
>
>De: "Anita Laera" <[email protected]>
>>À: "users" <[email protected]>
>>Envoyé: Lundi 13 Mars 2017 16:41:14
>>Objet: [ot-users] Evaluations vs. iterations - FORM
>>
>Hi all, 
>>I am performing FORM analysis and I am setting the maximum number of 
>>iterations to 100. In many occasions I see that the performed number of 
>>evaluations is larger than 100.
>>My questions are two:
>>1- what does the number of evaluation represents and in what it differs from 
>>the iterations?
>>2- I can set the max number of iterations as a convergence criterion but is 
>>there a way to control the evaluations?
>>
>>Thank you for your time!
>>
>>______________________________ _________________
>>OpenTURNS users mailing list
>>[email protected]
>>http://openturns.org/mailman/ listinfo/users
>>
>
>
>-- 
>
>Julien Schueller
>Phimeca Engineering
>www.phimeca.com

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