noisyCE2: Cross-Entropy Optimisation of Noisy Functions
In noisyCE2: Cross-Entropy Optimisation of Noisy Functions

Description Usage Arguments Value Methods (by generic) Examples

View source: R/noisyCE2.R

Unconstraint optimisation of noisy functions through the cross-entropy algorithm.

noisyCE2(
  f,
  domain,
  ...,
  rho = 0.05,
  N = 1000,
  smooth = NULL,
  stopwindow = tail(gam, (n > 20) * n/2),
  stoprule = ts_change(x),
  maxiter = 1000,
  maximise = TRUE,
  verbose = "v"
)

## S3 method for class 'noisyCE2'
print(x, ...)

## S3 method for class 'noisyCE2'
summary(object, ...)

## S3 method for class 'noisyCE2'
plot(x, what = c("x", "gam", "param"), start = NULL, end = NULL, ...)

## S3 method for class 'noisyCE2'
coef(object, ...)

`f`	objective function which takes the vector of optimisation variables as first argument.
`domain`	a `list` (or other coercible objects) where each component specifies the domain of each variable of the objective function `f`. The components of the list may be either objects of `typevar` class (see type_variable) or strings identifying one of type_variable functions (for example `"real"` for function `type_real()`). See § Examples.
`...`	other arguments to be passed to `f` or to other methods (for `print` and `plot`).
`rho`	parameter ρ of the Cross-Entropy algorithm. This argument may be passed either as a numeric value in (0,1) or as an unevaluated expression which may include the number of current iteration `n`, or the argument `N`.
`N`	parameter N of the Cross-Entropy algorithm. This argument may be passed either as a positive integer or as an unevaluated expression which may include the number of current iteration `n`.
`smooth`	list of unevaluated expressions to be used as smoothing rules for the parameters of the sampling probability distributions of all variables. If not `NULL`, all default or set smoothing rules of all variables will be overwritten. See type_variable for details and examples.
`stopwindow`	unevaluated expression returning the object to be passed to the stopping rule. Symbol `gam` permits the time series γ_t to be used (as a `numeric` vector).
`stoprule`	stopping rule passed as an unevaluated expression including `x` as the object returned by evaluation of argument `stopwindow`. The algorithm is stopped when zero is returned by the evaluation of `stoprule`. If returned object has attribute `mess`, this is used as a message. Currently, built-in stopping rules are `ts_change()` and `geweke()`, others may be defined by user.
`maxiter`	maximum number of iteration. When it is reached, algorithm is stopped whether or not the stopping criterion is satisfied. If the maximum number of iteration is reached, the `code` and the `message` components of `noisyCE` object are overwritten.
`maximise`	if `TRUE` (default) `f` is maximised, otherwise a minimisation of `f` is performed.
`verbose`	algorithm verbosity (values `v`, `vv` and `vvv` are admitted).
`x, object`	object of class `noisyCE2`, as returned by `noisyCE2`.
`what`	type of plot should be drawn. If `what = "x"` (default), values of the variables are plotted as time series; if `what = "gam"`, time series of statistics γ is plotted; if `what = "param"`, time series of parameters of the sampling distributions are plotted.
`start, end`	first and last value to be plotted. If `NULL`, all values are plotted.

An object of class noisyCE2 structured as a list with the following components:

`f`	argument `f`.
`fobj`	objective function `f` where possible arguments passed through argument `...` have been substituted. Thus, the value of the objective function maximised by `noisyCE` in `x0` can be computed as `fobj(x0)`. If a minimisation has been performed, `fobj` returns `f` with sign inverted.
`xopt`	`numeric` vector with solution.
`hxopt`	matrix of `niter` rows and `length(xopt)` columns with values of variables generated by the optimisation algorithm.
`param`	`list` of `length(xopt)` components where time series of parameters (vectors v_t) are stored for each variable as `data.frame` objects with `niter+1` rows (the first rows are the starting values set through function `noisyCEcontrol`).
`gam`	vector of values γ_t.
`niter`	number of iterations.
`code`	convergence code of the algorithm. Value `0` means that algorithm has converged; other values are defined according to the stopping rule.
`convMess`	textual message associated to the convergence code (if any).
`compTimes`	named vector computation times of each phase.

print: display synthetic information about a noisyCE2 object
summary: display summary information about a noisyCE2 object
plot: plot various components of a noisyCE2 object
coef: get the solution of the optimisation

library(magrittr)
# Optimisation of the 4-dimensional function:
# f(x1,x2,x3,x4)=-(x1-1)^2-(x2-2)^2-(x3-3)^2-(x4-4)^2
sol <- noisyCE2(function(x) -sum((x - (1:4))^2), domain = rep('real', 4))
# Representation of the convergence process:
plot(sol, what = 'x')
plot(sol, what = 'gam')