neldermead arguments in R - r

Question

I have found no examples of neldermead() on the internet, so I figured I would post the following. That said, I cant figure out how to control the max number of iterations of the algorithm.
https://cran.r-project.org/web/packages/neldermead/neldermead.pdf
Which states the following:
optbase An object of class ’optimbase’, i.e. a list created by optimbase() and containing the following elements:
iterations The number of iterations.
Working example of using Nelder Mead to fit a parabola by minimizing the residuals
library(neldermead); library(nloptr);
## ========= Minimizing the residuals for a 2d quadrature =========== ##
x2d = seq(-4,6,length.out=50); ## x vector definition
ynoise = runif(n=50, min=-2, max = 2) ## noise
y2d = 1.3 + (x2d-2.1)^2 + ynoise ## y data for fitting
## Fitting with nelder-mead
quadmin <- function(x){ sum( (y2d - x[1] - (x2d - x[2])^2)^2 ) }
x000 <- c(1, 2)
sol2d <- neldermead(x0 = x000, fn = quadmin)
sol2d
yfit = sol2d[[1]][1] + (x2d - sol2d[[1]][2])^2 ## Fitted curve.
plot(x2d, y2d); lines(x2d, yfit) ## Plotting
But I'm looking to do something like:
sol2d <- neldermead(x0 = x000, fn = quadmin, iterations = 200)
^^^ which doesn't work. Neither does putting it into a list:
sol2d <- neldermead(x0 = x000, fn = quadmin, optbase = list(iterations = 200))
This is a basic question about how to use these arguments, so I apologize if this isn't the right title. In advance, thank you for your help.

Answer 1

There are at least tow neldermead functions available in R. One is from the package neldermead which correspond to the documentation you link.
I have not been able to make it work. It gives me back neither error or solution.
The code:
library(neldermead)
library(nloptr)
## ========= Minimizing the residuals for a 2d quadrature =========== ##
x2d = seq(-4,6,length.out=50); ## x vector definition
ynoise = runif(n=50, min=-2, max = 2) ## noise
y2d = 1.3 + (x2d-2.1)^2 + ynoise ## y data for fitting
## Fitting with nelder-mead
quadmin <- function(x){
sum(y2d - x[1] - (x2d - x[2])^2)^2 }
x000 <- c(1, 2)
opt <- optimbase(x0 = as.matrix(x000),fx0 = -1000,maxiter = 200,fopt = quadmin,verbose=T)
sol2d <- neldermead::neldermead(opt)
On the other hand, package nloptr also provides a neldermedad function which sintax looks closer to you code and I have been able to run:
library(neldermead)
library(nloptr)
## ========= Minimizing the residuals for a 2d quadrature =========== ##
x2d = seq(-4,6,length.out=50); ## x vector definition
ynoise = runif(n=50, min=-2, max = 2) ## noise
y2d = 1.3 + (x2d-2.1)^2 + ynoise ## y data for fitting
## Fitting with nelder-mead
quadmin <- function(x){
sum(y2d - x[1] - (x2d - x[2])^2)^2 }
x000 <- c(1, 2)
sol2d <- nloptr::neldermead(x0 = x000,fn =quadmin,control =list(maxeval=200))
yfit = sol2d[[1]][1] + (x2d - sol2d[[1]][2])^2 ## Fitted curve.
plot(x2d, y2d); lines(x2d, yfit)
As you can see, the only issue with you code was the control part.
Best!