KLWFMult: Cocktail Algorithm for KL-Optimality

In optedr: Calculating Optimal and D-Augmented Designs for Single- and Multi-Factor Models

Cocktail Algorithm for KL-Optimality

Description

Cocktail Algorithm for KL-Optimality

Usage

KLWFMult(
  init_design,
  kl_fun,
  beta2_init,
  lower,
  upper,
  design_space,
  grid.length,
  join_thresh,
  delete_thresh,
  delta_weights,
  tol,
  tol2,
  max_iter,
  kl_meta = list(type = "kl_fun")
)

Arguments

init_design	optional dataframe with the initial design for the algorithm.
kl_fun	function(x, beta2); returns the KL divergence at design point x given rival parameters beta2.
beta2_init	numeric vector of initial rival parameter values.
lower	lower bounds for rival parameters in the inner optimisation.
upper	upper bounds for rival parameters in the inner optimisation.
design_space	named list with bounds for each design variable.
grid.length	numeric value for the sensitivity search grid / LHS size.
join_thresh	numeric value for the merge heuristic.
delete_thresh	numeric value for the weight deletion threshold.
delta_weights	numeric value in (0, 1), parameter of the algorithm.
tol	numeric value for convergence of the weight loop.
tol2	numeric value for the outer stop condition.
max_iter	maximum number of outer iterations.
kl_meta	list with summary metadata (type, and optionally family and phi for the standard path).

Value

An object of class optdes.

See Also

Other cocktail algorithms: CWFMult(), DWFMult(), DsWFMult(), IWFMult(), WFMult()

optedr documentation built on June 23, 2026, 5:07 p.m.