Re: GSoC 2015: Optimization Package: Non-linear and constrained least squares lsqcurvefit, lsqlin, lsqnonlin

[Olaf Till]

On Wed, Feb 25, 2015 at 07:56:15PM +0000, Nir Krakauer wrote:
> Olaf, I agree that we do not expect to reproduce the Matlab results in all
> cases, especially in more 'difficult' or ill-posed problems -- but this is
> also true for functions in core Octave that aim to have the same syntax as
> Matlab, for example inverting an almost singular matrix may well give
> different numbers in Octave than in Matlab. Numerical approximations will
> unavoidably have some implementation dependence.
> 
> In terms of algorithms, it looks like the Optimization package has
>  __lm_svd__, which offers a Levenberg-Marquardt algorithm for least squares
> (with arbitrary constraints allowed, if I understand it correctly?). The
> other algorithm Matlab offers in lsqcurvefit and  lsqnonlin is a
> trust-region one[1] which supposedly can be more efficient for
> high-dimensional problems -- is anything like that implemented in Octave?
> 
> --Nir
> 
> [1] 
> *http://www.mathworks.com/help/optim/ug/least-squares-model-fitting-algorithms.html#broz0i4
> <http://www.mathworks.com/help/optim/ug/least-squares-model-fitting-algorithms.html#broz0i4>*

We have no trust region algorithm. But to be answerable for trust
region algorithms I'd need to do some studying first.

Olaf

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