Hi Paul
I've been using the latest Scilab stable release on my working station;
nevertheless you're right I get 12 when running the code on my laptop
(same release but under Linux).
Paul
Le 2017-01-13 17:11, Paul Bignier a écrit :
> 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 [1 [1]] 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.carr...@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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>
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> Paul BIGNIER
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