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

[AsmaA]

Hi,

Apologies for the delayed response. I was travelling and on holiday.


bpabbott wrote
>> On Apr 6, 2015, at 3:05 PM, Carnë Draug <

> carandraug@

> > wrote:
>> 
>> On 6 April 2015 at 19:57, Ben Abbott <

> bpabbott@

> > wrote:
>>>> On Feb 24, 2015, at 3:42 AM, Olaf Till <

> i7tiol@

> > wrote:
>>>> 
>>>>> 
>>>>> I am studying the Levenberg-Marquardt algorithm from [2].
>>>> 
>>>> This seems to be a general introduction to LM. An actual algorithm
>>>> must also, among others, take measures to be numerically stable. It is
>>>> usually best to start from one of the several already existing
>>>> algorithms (I know this is your intention). In the optim package we
>>>> have, among others, an SVD-based algorithm.
>>> 
>>> I came across an Levenberg-Marquardt implementation for Octave.
>>> 
>>>        https://sites.google.com/site/ulfgri/numerical/levmar
>>> 
>>> Perhaps it is useful.
>>> 
>> 
>> leasqr [1] from optim package provides an implementation of
>> Levenberg-Marquardt nonlinear least squares algorithm.
>> 
>> I have used it plenty of times as replacement for Matlab's nlinfit
>> and get results which are comparable within machine precision.
>> 
>> Carnë
>> 
>> [1] http://octave.sourceforge.net/optim/function/leasqr.html
> 
> Admittedly, I've not used leasqr enough to be comfortable with all its
> capabilities.
> 
> For example, I hadn't realized that it handled constraints (linear,
> inequality, and box constraints are all supported).
> 
> Ben

Thank-you for highlighting leasqr.

I have added a note in the GSoC project plan on the wiki for a 'nlinfit'
implementation around 'leasqr'.

Regards,
AsmaA



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