[Scilab-users] Optim use and 'err' flag

[Paul Bignier]

To complete my previous answer, the error arose from the built-in line 
search algorithm (err>10 means different flavors of it failing). In your 
case, it is pointing to a "too small deltaT", so yes it looks like your 
algorithm is converging but it has trouble finishing.

You can get more info by using "imp=3" in your call to optim (as 
documented). If you are using a nightly build then use "iprint=3" (the 
online doc hasn't been updated yet).

Best regards,

Paul


On 01/13/2017 05:11 PM, Paul Bignier wrote:
>
>
> Hello Paul,
>
> Running your script gives me "err=12", which is not documented but I 
> don't get how you got 3?
>
> I see though that you reached 'evals' & 'iters', perhaps optim wanted 
> to continue but was capped by those.
>
> Feel free to use the format 
> <https://help.scilab.org/docs/6.0.0/en_US/format.html> function to get 
> more on-screen precision to your values.
>
> I will surely commit something soon in order to fix the "12" flag.
>
> Have a good evening,
>
> Paul
>
>
> On 01/13/2017 02:39 PM, paul.carrico at free.fr wrote:
>> Hi all
>>
>> I’m trying to improve how to use Optim in Scilab, so I’m still using 
>> the basic Rosembrock function; in the example hereafter, one can see 
>> that Optim goes back the Error flag to 3 and I do not understand why?
>>
>> The goal is to be able to check all the values of this flag in order 
>> to validate the result ; while the values are the optimized ones, the 
>> calculation indicates that the optimization fails …
>>
>> I’m a bit loss … so any feedback will be appreciated
>>
>> Thanks
>>
>> Paul
>>
>> ################################################################################### 
>>
>> In my understanding:
>> -    err = 9 : everything went well … ok
>>
>> -    err = 3 : Optimization stops because of too small variations for x
>> -    err=1 : Norm of projected gradient lower than …
>> -    err=2 : At last iteration f decreases by less than …
>> -    err=4 : Optim stops: maximum number of calls to f is reached ==> 
>> increase nocf
>> -    err=5 : Optim stops: maximum number of iterations is reached. 
>> ==> increase niter
>> -    err=6 : Optim stops: too small variations in gradient direction.
>> -    err=7 : Stop during calculation of descent direction.
>> -    err=8 : Stop during calculation of estimated hessian.
>> -    err=10 : End of optimization (linear search fails).
>>
>>
>>
>> // Rosembrock function
>> function f=rosembrock(x)
>>     f = ( 1 - x(1))^2 + 100*( x(2)-x(1)^2 )^2;
>> endfunction
>>
>> // Cost function
>> function [f, g, ind]=cost(x, ind)
>>     f = rosembrock(x);
>>     //        g = derivative(rosembrock, x.',order = 4);
>> //            g = numderivative(rosembrock, x.',order = 4);
>>     g = numderivative(rosembrock, x.',0.1, order = 4);
>> endfunction
>>
>> initial_parameters = [10 100]
>> lower_bounds = [0 0];
>> upper_bounds = [1000 1000];
>> nocf = 100000;      // number of call of f
>> niter = 100000;    // number of iterations
>> [fopt, xopt, gopt, work, iters, evals, err] = 
>> optim(cost,'b',lower_bounds,upper_bounds,initial_parameters,'qn','ar',nocf,niter);
>> xopt
>> fopt
>> iters
>> evals
>> err
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>
> -- 
> Paul BIGNIER
> Development engineer
> -----------------------------------------------------------
> Scilab Enterprises
> 143bis rue Yves Le Coz - 78000 Versailles, France
> Phone: +33.1.80.77.04.68
> http://www.scilab-enterprises.com
>
>
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-- 
Paul BIGNIER
Development engineer
-----------------------------------------------------------
Scilab Enterprises
143bis rue Yves Le Coz - 78000 Versailles, France
Phone: +33.1.80.77.04.68
http://www.scilab-enterprises.com

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