[R] constrained nonlinear optimisation in R?

[Spencer Graves]

      Other alternatives to the R library for quadratic programming: 

      1.  What are the nature of your constraints?  "optim" will 
optimize a function with optional box constraints.  "constrOptim" will 
optimize a function subject to linear inequality constraints. 

      2.  If you want to estimate the p[i]'s, i = 1, ..., k, I would 
recommend a multivariate logistic transformation to (k-1) unconstrained 
variables.  I have had serious difficulties with constrained optimizers 
testing values outside the constraints and then stopping because the 
objective function misbehaved.  I don't know if "optim" does this, but I 
don't even try constrained optimization if I can find a sensible, 
unconstrained parameterization.  Often, confidence regions, etc., are 
better behaved in the unconstrained space as well. 

hope this helps.  spencer graves


Simon Wood wrote:

>>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
>>>>        
>>>>
>
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