[R] R & gams

[Jason Barnhart]

Any answer to this question will be insufficient without more detail
regarding the nature of the models you are investigating.

As mentioned in prior posts GAMS does solve large-scale and
computationally intensive optimization and math programming problems.

    One strength is its ability to interface with different
    mathematical solvers.  The choice of solver depends on the nature
    of the desired optimization.  For example, (by the way, these
    statements are broad generalizations to (help) illuminate the path
    of discovery; they are not meant to be exhaustive; additionally
    I've not used GAMS in many years so things have changed) at one
    time, CONOPT was a great choice for sparse, large-scale non-
    linear (NLP) problems while CPLEX was better suited for linear
    programming (LP).  In this role GAMS facilitates interfacing with
    either solver.

    A second strength of GAMS is that users can focus on structuring
    problems unique to their domain rather than developing solver
    programs. Which means it is a power tool and users need some
    fundamental knowledge of optimization.

One's *initial* take regarding GAMS as a solution implies applied
optimization/operations research types of problems such as: how many
trucks do I need to deliver X gallons of milk to Y cities? ... and so
on.

However, models and their solutions take many forms so GAMS is not
constrained to these types of problems only.  In particular, I've seen
solutions to maximum entropy regression performed in GAMS where the
data are not large-scale, but the computational requirements may have
dictated using an industrial-strength solver.

R has embedded solvers (see ?optim), can interface with external
solvers(through compilation and linking) and can probably structure
problems sufficiently to send data to a solver.  As Sarah Goslee
mentioned in a prior post R *might* not do *all* of those things as
efficiently as in GAMS.

To get a better answer you'll have to do something like the following:
    1) Conduct some research on the exact nature of your model(s).
    2) Determine what functionality GAMS is providing and ascertain why
       GAMS is crucial to their solution.
    3) Understand R a little better to see if it can provide the
       corresponding functionality.
    4) Determine if time or money is your primary constraint.  If it
       is time, then purchasing a GAMS solution to get to market faster
       is probably better.  If it is money then an investment in time (with
       R) may be the better option, but you'll have evaluate these options.
    5) Investigate other commercial packages like AMPL, etc.

If you're doing research either solution may suffice.  Additionally, you may
solve the model in either GAMS or R, but choose to deploy in an altogether
different language like C++.

Hope that helps, sorry for the novel.
-jason

----- Original Message ----- 
From: "vittorio" <vdemart1 at tin.it>
To: <r-help at stat.math.ethz.ch>
Sent: Friday, October 27, 2006 2:33 PM
Subject: Re: [R] R & gams


The people presenting the models cited as a reference the site www.gams.com
which, as you said, is about high-level modeling system for mathematical
programming and optimization.

Vittorio

Alle 17:52, venerdì 27 ottobre 2006, Ravi Varadhan ha scritto:
> Can you be more specific about what you mean by "gams"?  Do you mean
> generalized additive models (GAM)?  If so, R is a good environment for
> forecasting models and GAM.  However, the link that you provided is NOT 
> for
> generalized additive modeling, but it is for General Algebraic Modeling
> System (GAMS), which is a high-level modeling system for mathematical
> programming and optimization.
>
> Ravi.
>
> ---------------------------------------------------------------------------
>- -------
>
> Ravi Varadhan, Ph.D.
>
> Assistant Professor, The Center on Aging and Health
>
> Division of Geriatric Medicine and Gerontology
>
> Johns Hopkins University
>
> Ph: (410) 502-2619
>
> Fax: (410) 614-9625
>
> Email: rvaradhan at jhmi.edu
>
> Webpage:  http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html
>
>
>
> ---------------------------------------------------------------------------
>- --------
>
>
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of vittorio
> Sent: Friday, October 27, 2006 3:14 PM
> To: r-help at stat.math.ethz.ch
> Subject: [R] R & gams
>
> At office I have been introduced by another company  to new, complex 
> energy
> forecasting models using gams as the basic software.
> I have been told by the company offering the models that gams is
> specialised
>
> in dealing with huge, hevy-weight linear and non-linear modelling (see an
> example in http://www.gams.com/modtype/index.htm) and they say it is 
> almost
> the only option for doing it.
>
> I would like to know your opinion on the subject and, above all, if R can
> be
>
> an effective alternative and to what extent, if any.
>
> Thanks
> Vittorio
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
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> PLEASE do read the posting guide
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> minimal,
> self-contained, reproducible code.

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