optimization with inequalities
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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