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

[paul.carrico at free.fr]

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.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
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> Scilab Enterprises
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