ljoptim: (Adaptive) Version of Luus-Jakola Optimization

In cops: Cluster Optimized Proximity Scaling

(Adaptive) Version of Luus-Jakola Optimization

Description

Adaptive means that the search space reduction factors in the number of iterations; makes convergence faster at about 100 iterations

Usage

ljoptim(
  x,
  fun,
  ...,
  red = ifelse(adaptive, 0.99, 0.95),
  lower,
  upper,
  acc = 1e-06,
  accd = 1e-04,
  itmax = 1000,
  verbose = 0,
  adaptive = TRUE
)

Arguments

x	optional starting values
fun	function to minimize
...	additional arguments to be passed to the function to be optimized
red	value of the reduction of the search region
lower	The lower contraints of the search region
upper	The upper contraints of the search region
acc	if the numerical accuracy of two successive target function values is below this, stop the optimization; defaults to 1e-6
accd	if the width of the search space is below this, stop the optimization; defaults to 1e-4
itmax	maximum number of iterations
verbose	numeric value hat prints information on the fitting process; >2 is extremely verbose
adaptive	should the adaptive version be used? defaults to TRUE.

Value

A list with the components (see also optim)

• par The position of the optimimum in the search space (parameters that minimize the function; argmin fun)

• value The value of the objective function at the optimum (min fun)

• counts The number of iterations performed at convergence with entries fnction for the number of iterations and gradient which is always NA at the moment

• convergence 0 successful completion by the accd or acc criterion, 1 indicate iteration limit was reached, 99 is a problem

• message is NULL (only for compatibility or future use)

Examples

fbana <- function(x) {
x1 <- x[1]
x2 <- x[2]
100 * (x2 - x1 * x1)^2 + (1 - x1)^2
}
res1<-ljoptim(c(-1.2,1),fbana,lower=-5,upper=5,accd=1e-16,acc=1e-16)
res1

set.seed(210485)
fwild <- function (x) 10*sin(0.3*x)*sin(1.3*x^2) + 0.00001*x^4 + 0.2*x+80
plot(fwild, -50, 50, n = 1000, main = "ljoptim() minimising 'wild function'")
res2<-ljoptim(50, fwild,lower=-50,upper=50,adaptive=FALSE,accd=1e-16,acc=1e-16)
points(res2$par,res2$value,col="red",pch=19)
res2

cops documentation built on Jan. 22, 2023, 1:47 a.m.