Hi All,
For a non-linear minimization optimization problem that I have, I am
getting better objective function value in Excel(15) as compared to
nloptr (73).
the nloptr is setup as:
opts = list("algorithm"="NLOPT_LN_COBYLA",
"xtol_rel"=1.0e-8, "maxeval"= 10000)
lb = vector("numeric",length= length(my.data.var))
result <- nloptr(my.data.var,eval_f = Error.func,lb=lb,
ub =
c(Inf,1,1,1,1,1,Inf,1,1,1,1,1,Inf,1,1,1,1,1,Inf,1,1,1,1,1),eval_g_ineq=constraint.func,opts
= opts)
As observed even with 10000 as maximum evaluations, the objective
function is way off as compared to Excel's GRG which solved it in 200
iterations.
Is there a way to improve the objective function value from nloptr? OR
is there any excel's GRG equivalent package in R.
Thanks for your time!
PD
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