Passing parameters to nloptr objective function - R

Question

I intend to use nloptr package in a forloop as below:

for(n in 1:ncol(my.data.matrix.prod))
 {
 alpha.beta <- as.vector(Alpha.beta.Matrix.Init[,n])

 opts = list("algorithm"="NLOPT_LN_COBYLA",
             "xtol_rel"=1.0e-8, "maxeval"= 2000)

 lb = vector("numeric",length= length(alpha.beta))

 result <- nloptr(alpha.beta,eval_f = Error.func.oil,lb=lb,
                  ub = c(Inf,Inf),eval_g_ineq=Const.func.oil,
                  opts = opts)

 Final.Alpha.beta.Matrix[,n] <-   result$solution    
  }

Apart from passing the "optimization parameters: alpha.beta" to the error function(minimization function) , I also would like to send n from the forloop. Is there anyway to do this?

The error func is defined as:

    Error.func.oil <- function(my.data.var,n)        
 {
   my.data.var.mat <- matrix(my.data.var,nrow = 2,ncol = ncol(my.data.matrix.prod) ,byrow = TRUE)

   qo.est.matrix <-  Qo.Est.func(my.data.var.mat)
   diff.values <- well.oilprod-qo.est.matrix    #FIND DIFFERENCE BETWEEN CAL. MATRIX AND ORIGINAL MATRIX
   Error <- ((colSums ((diff.values^2), na.rm = FALSE, dims = 1))/nrow(well.oilprod))^0.5    #sum of square root of the diff

   Error[n]
 }

The constraint function is simple and defined as:

Const.func.oil <- function(alpha.beta)
 {
    cnst <- alpha.beta[2]-1
    cnst
 }

So, when I run the above code, I get an error

Error in .checkfunargs(eval_f, arglist, "eval_f") : eval_f requires argument 'n' but this has not been passed to the 'nloptr' function.

How do I pass "n" to the error function? note that "n" is not to be optimized. It's just an index.

Answer 1

Okay. I read some examples online and found out that I can probably mention "n" in the definition of nloptritself as:

for(n in 1:ncol(my.data.matrix.prod))
 {
 alpha.beta <- as.vector(Alpha.beta.Matrix.Init[,n])

 opts = list("algorithm"="NLOPT_LN_COBYLA",
             "xtol_rel"=1.0e-8, "maxeval"= 5000)

 lb = c(0,0)

 result <- nloptr(alpha.beta,eval_f = Error.func.oil,lb=lb,
                  ub = c(Inf,Inf),
                  opts = opts, n=n)  #Added 'n' HERE

 Final.Alpha.beta.Matrix[,n] <-   result$solution    
}

This seems to have worked for me. Hence, I am setting this as closed.