Message-ID: <971536df0511280830o48497052m2c59c963c0f0a411@mail.gmail.com>
Date: 2005-11-28T16:30:43Z
From: Gabor Grothendieck
Subject: optimization with inequalities
In-Reply-To: <20051128152226.9786.qmail@web26801.mail.ukl.yahoo.com>
If I understand this correctly the variables over which
you are optimizing are p[1], p[2] and p[3] whereas x and y
are fixed and known during the optimization. In that case its
a linear programming problem and you could use the lpSolve
library which would allow the explicit specification of the
constraints.
On 11/28/05, Florent Bresson <f_bresson at yahoo.fr> wrote:
> I have to estimate the following model for several
> group of observations :
>
> y(1-y) = p[1]*(x^2-y) + p[2]*y*(x-1) + p[3]*(x-y)
>
> with constraints :
> p[1]+p[3] >= 1
> p[1]+p[2]+p[3]+1 >= 0
> p[3] >= 0
>
> I use the following code :
> func <- sum((y(1-y) - p[1]*(x^2-y) + p[2]*y*(x-1) +
> p[3]*(x-y))^2)
> estim <- optim( c(1,0,0),func, method="L-BFGS-B" ,
> lower=c(1-p[3], -p[1]-p[3]-1, 0) )
>
> and for some group of observations, I observe that the
> estimated parameters don't respect the constraints,
> espacially the first. Where's the problem please ?
>
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