[SciPy-User] scipy.optimize.root with method = 'lm' problem

[Matthieu Brucher]

Hi,

There isn't much to advise you without any data or way to reproduce this.
As a small value is added to the Jacobian to keep it positive semi-definite
in the algorithm, the fact that it diverges seems to indicate that the
eigenvalues of your system are not (or it is a bug, but for this, we will
need more data to reproduce the issue).
Can you output at each stage your Jacobian and check that it doesn't
present that kind of behavior?

Cheers,

Matthieu


2016-12-27 15:13 GMT+01:00 William Heymann <immudzen at gmail.com>:

> I am using scipy.optimize.root with method = 'lm' and jac=True and the
> descent algorithm looks like it is taking steps that are far too large.
>
> I have 10 variables and my goal function returns a vector of 4374 outputs
> and 4374x10 for the jacobian. The initial jacobian matches the same one I
> generated when I use lsqnonlin in matlab and that works just fine. Instead
> with scipy I am getting no changes at all for the first two iterations and
> then suddenly a huge jump to basically the upper end of the double range
> which seems pretty extreme.
>
> I would really like to make this work with scipy and I have my function
> along with the exact derivatives for the jacobian computed with AD. I have
> also looked at the singular values of the jacobian and they are all
> positive and non-zero so the system should be locally convex at least.
>
> This is the call I make to scipy and it seems reasonable
>
> sol = scipy.optimize.root(residual, start, args=(large, small, weight,
> data), method='lm', jac=True, options={'ftol':1e-6, 'xtol':1e-6})
>
> Thank you
>
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