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
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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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