Hello.
>
> >
> > i'm implementing in java a model which was originally developped in Matlab.
> >
> > The goal is to minimize a non-differentiable trivariate real function.
> > The Matlab code calls the fminunc function
> > x = fminunc(fun,x0,options)
> > with x0 = [a, b, c] the initial guess, and
> > options as 'MaxFunEvals' to 500
> >
> > I don't think the function is differentiable.
> > But i'm not very skilled in math
> > and mainly not at ease with the different parameters required for
> > optimizers creation...
> >
> > Could someone advise me about which to use ?
>
> Look at either NelderMeadSimplex, MultiDimensionalSimplex or
> CMAESOptimizer in the org.apache.commons.math3.optimization.direct package.
The easiest would be to start with "PowellOptimizer" (in the same package).
Code would be like:
---CUT---
MultivariateOptimizer optim = new PowellOptimizer();
MultivariateFunction f = ... your function ...
PointValuePair result = optim.optimize(500, f, GoalType.MINIMIZE,
new double[] { a, b, c});
double[] minimum = result.getPoint();
---CUT---
Regards,
Gilles
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