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Optimizer Question

4 messages · Roger Koenker, Bromaghin, Jeffrey, Hesen Peng +1 more

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You don?t say anything about the nature of your problem domain beyond its size,
but for convex problems Mosek is a good option, and there is an R interface
called Rmosek that is quite convenient.

url:    www.econ.uiuc.edu/~roger            Roger Koenker
email    rkoenker at uiuc.edu            Department of Economics
vox:     217-333-4558                University of Illinois
fax:       217-244-6678                Urbana, IL 61801
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My apologies!  The structure is close to linear, but there is a scaling so
that modeled proportions sum to 1.0, so the problem is nonlinear.  There
are quite a few linear constraints as all parameters are non-negative and
subsets of the
parameters must sum to 1.

Best regards,
Jeff

-----------------------------------------------
Jeffrey F. Bromaghin, PhD
Research Statistician
USGS Alaska Science Center
Marine Ecosystems Office
4210 University Drive
Anchorage, AK 99508
907-786-7086
jbromaghin at usgs.gov
*http://alaska.usgs.gov/science/biology/quantitative_ecology/index.php
<http://alaska.usgs.gov/science/biology/quantitative_ecology/index.php>*

On Fri, Aug 4, 2017 at 12:35 PM, Roger Koenker <rkoenker at illinois.edu>
wrote:

  
  
1 day later
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I apologize since this answer is kind of off topic. If you look beyond the
boundary, TensorFlow and Theano have been very popular these days in the
deep learning field and they can be used to solve optimization problems.
They are being used to solve problems involving millions of parameters. And
I do thing ecosystem-wise they are better than using R packages.




On Fri, Aug 4, 2017 at 1:45 PM, Bromaghin, Jeffrey <jbromaghin at usgs.gov>
wrote:

  
    
#
Hi Hesen,

I am not sure if the premise "..ecosystem-wise they are better than
using R packages..". First of all almost all of the optimisation
techniques as far as I know in (deep) machine learning are the
gradient base. R provides superb ecosystem for gradient free
optimisation and other constraint based optimisations where Keras or
tensorflow solves. Yes, they scale better in Gradient decent base
algorithms but they don't cover the spectrum R ecosystem provides, at
least not as of today.  Have a look at the spectrum of optimisations R
ecosystem provides:
https://cran.r-project.org/web/views/Optimization.html
It is pretty diverse.

Cheers,
-m
On 5 August 2017 at 23:12, Hesen Peng <hesen.peng at gmail.com> wrote: