R help - Jul 2008 - Non-linearly constrained optimisation

Dear R Users,
I am looking for some guidance on setting up an optimisation in R with
non-linear constraints.

Here is my simple problem:
- I have a function h(inputs) whose value I would like to maximise
- the 'inputs' are subject to lower and upper bounds
- however, I have some further constraints: I would like to constrain the
values for two other separate function f(inputs) and g(inputs) to be within
certain bounds

This means the 'inputs' must not only lie within the bounds specified by
the 'upper' and 'lower' bounds, but they must also not take on
values such
that f(inputs) and g(inputs) take on values outside defined values. h, f
and g are all non-linear.

I believe constroptim would work if f and g were linear. Alas, they are
not. Is there any other way I can achieve this in R ?

Thanks in advance,
Tolga

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Tolga,
Your issue seems to be a common one at present. While I am relatively new  
to R (and would welcome being corrected), I haven't been able to find an  
existing module to parse algebraic equations and build acyclic networks  
(for the objective function and each constraint) to submit to solving  
routines (such as optim, BB, Patrick Burns' genopt from S Poetry, Algencan  
etc).  Certainly there are the components to build one, for example  
topological sort packages like mathgraph and Carter Butts' network. I have  
implemented acyclic networks in three major projects and so I have started  
to contemplate the missing package in both R and Mathematica. At this  
point I am significantly further advanced in Mathematica, building from  
Eric Swanson's excellent perturbationAIM package. Yet it seems slightly  
odd to me that the required functionality hasn't been developed in R up to  
this point in time. Many people need this functionality, the network  
algorithms have been around for forty years and there are many solvers,  
even open source ones like ipopt. Of course, the "big guns" in this
field
are GAMS and AMPL and it is perhaps their overwhelming presence or respect  
for the developers of these packages that has led R developers to be  
somewhat cautious about releasing code in this area. However, there are  
already alternatives. For a quasi open source version of AMPL you could  
use Dr Ampl (http://www.gerad.ca/~orban/drampl/ ) or write your problem in  
GAMS or AMPL format and submit to the Neos server either directly  
(http://neos.mcs.anl.gov/neos/) or by using pyneos.py  
(www.gerad.ca/~orban/pyneos/pyneos.py). I really hope this gets worked out  
in R at some stage!
Stuart

On Sun, 20 Jul 2008 08:10:35 +1000, <tolga.i.uzuner at jpmorgan.com>
wrote:
> Dear R Users,
> I am looking for some guidance on setting up an optimisation in R with
> non-linear constraints.
>
> Here is my simple problem:
> - I have a function h(inputs) whose value I would like to maximise
> - the 'inputs' are subject to lower and upper bounds
> - however, I have some further constraints: I would like to constrain the
> values for two other separate function f(inputs) and g(inputs) to be  
> within
> certain bounds
>
> This means the 'inputs' must not only lie within the bounds
specified by
> the 'upper' and 'lower' bounds, but they must also not take
on values
> such
> that f(inputs) and g(inputs) take on values outside defined values. h, f
> and g are all non-linear.
>
> I believe constroptim would work if f and g were linear. Alas, they are
> not. Is there any other way I can achieve this in R ?
>
> Thanks in advance,
> Tolga
>
> Generally, this communication is for informational purposes only
> and it is not intended as an offer or solicitation for the purchase
> or sale of any financial instrument or as an official confirmation
> of any transaction. In the event you are receiving the offering
> materials attached below related to your interest in hedge funds or
> private equity, this communication may be intended as an offer or
> solicitation for the purchase or sale of such fund(s).  All market
> prices, data and other information are not warranted as to
> completeness or accuracy and are subject to change without notice.
> Any comments or statements made herein do not necessarily reflect
> those of JPMorgan Chase & Co., its subsidiaries and affiliates.
>
> This transmission may contain information that is privileged,
> confidential, legally privileged, and/or exempt from disclosure
> under applicable law. If you are not the intended recipient, you
> are hereby notified that any disclosure, copying, distribution, or
> use of the information contained herein (including any reliance
> thereon) is STRICTLY PROHIBITED. Although this transmission and any
> attachments are believed to be free of any virus or other defect
> that might affect any computer system into which it is received and
> opened, it is the responsibility of the recipient to ensure that it
> is virus free and no responsibility is accepted by JPMorgan Chase &
> Co., its subsidiaries and affiliates, as applicable, for any loss
> or damage arising in any way from its use. If you received this
> transmission in error, please immediately contact the sender and
> destroy the material in its entirety, whether in electronic or hard
> copy format. Thank you.
> Please refer to http://www.jpmorgan.com/pages/disclosures for
> disclosures relating to UK legal entities.
>
> ______________________________________________
> R-help at r-project.org mailing list
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> PLEASE do read the posting guide  
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.


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Please have a look at the 'Rdonlp2' package at
<http://arumat.net/Rdonlp2/>.
It provides non-linear optinization with nonlinear constraints, so it may be
exactly what you are looking for. 

Whether this algorithm is powerful enough for your task has to be seen. 
There are examples on the Web page that will enable a quick start.

You did not give information about the complexity of your task, nor whether
you are seeking local or global solutions.

Hans Werner Borchers
ABB Corporate Research



Dear R Users,
I am looking for some guidance on setting up an optimisation in R with
non-linear constraints.

Here is my simple problem:
- I have a function h(inputs) whose value I would like to maximise
- the 'inputs' are subject to lower and upper bounds
- however, I have some further constraints: I would like to constrain the
values for two other separate function f(inputs) and g(inputs) to be within
certain bounds

This means the 'inputs' must not only lie within the bounds specified by
the 'upper' and 'lower' bounds, but they must also not take on
values such
that f(inputs) and g(inputs) take on values outside defined values. h, f
and g are all non-linear.

I believe constroptim would work if f and g were linear. Alas, they are
not. Is there any other way I can achieve this in R ?

Thanks in advance,
Tolga


-----
----
Hans W. Borchers
ABB Corporate Research Germany
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