[R] constrained nonlinear optimisation in R?

[A.J. Rossini]

Simon Wood <simon at stats.gla.ac.uk> writes:

>> Hello.  I have searched the archives but have not found anything.  I
>> need to solve a constrained optimisation problem for a nonlinear
>> function (“maximum entropy formalism”).  Specifically,
>>
>> Optimise: -1*SUM(p_ilog(p_i)) for a vector p_i of probabilities,
>> conditional on a series of constraints of the form:
>>
>> SUM(T_i*p_i)=k_i  for given values of T_i and k_i  (these are
>> constraints on expectations).
>>
> A better answer may exist to this question, but here goes anyway....
> Could you use sequential quaratic programming here (i.e. just constrain
> the QP problem generated at each iterate of Newton's method)? There's an R
> library for quadratic programming....
>
> Simon
>
> _____________________________________________________________________
>> Simon Wood simon at stats.gla.ac.uk        www.stats.gla.ac.uk/~simon/
>>>  Department of Statistics, University of Glasgow, Glasgow, G12 8QQ
>>>>   Direct telephone: (0)141 330 4530          Fax: (0)141 330 4814
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://www.stat.math.ethz.ch/mailman/listinfo/r-help
>

help.search("constrained") suggests:

constrOptim(base)       Linearly constrained optimisation

which might do the trick.


-- 
rossini at u.washington.edu            http://www.analytics.washington.edu/ 
Biomedical and Health Informatics   University of Washington
Biostatistics, SCHARP/HVTN          Fred Hutchinson Cancer Research Center
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