mlr_loop_functions_mpcl: Single-Objective Bayesian Optimization via Multipoint Constant Liar

Description

Loop function for single-objective Bayesian Optimization via multipoint constant liar. Normally used inside an OptimizerMbo.

In each iteration after the initial design, the surrogate and acquisition function are updated. The acquisition function is then optimized, to find a candidate but instead of evaluating this candidate, the objective function value is obtained by applying the liar function to all previously obtained objective function values. This is repeated q - 1 times to obtain a total of q candidates that are then evaluated in a single batch.

Usage

bayesopt_mpcl(
  instance,
  surrogate,
  acq_function,
  acq_optimizer,
  init_design_size = NULL,
  q = 2L,
  liar = mean,
  random_interleave_iter = 0L
)

Value

invisible(instance)

The original instance is modified in-place and returned invisible.

Arguments

instance: (bbotk::OptimInstanceBatchSingleCrit)
The bbotk::OptimInstanceBatchSingleCrit to be optimized.
surrogate: (Surrogate)
Surrogate to be used as a surrogate. Typically a SurrogateLearner.
acq_function: (AcqFunction)
AcqFunction to be used as acquisition function.
acq_optimizer: (AcqOptimizer)
AcqOptimizer to be used as acquisition function optimizer.
init_design_size: (NULL | integer(1))
Size of the initial design. If NULL and the bbotk::ArchiveBatch contains no evaluations, 4 * d is used with d being the dimensionality of the search space. Points are generated via a Sobol sequence.
q: (integer(1))
Batch size > 1. Default is 2.
liar: (function)
Any function accepting a numeric vector as input and returning a single numeric output. Default is mean. Other sensible functions include min (or max, depending on the optimization direction).
random_interleave_iter: (integer(1))
Every random_interleave_iter iteration (starting after the initial design), a point is sampled uniformly at random and evaluated (instead of a model based proposal). For example, if random_interleave_iter = 2, random interleaving is performed in the second, fourth, sixth, ... iteration. Default is 0, i.e., no random interleaving is performed at all.

References

Ginsbourger, David, Le Riche, Rodolphe, Carraro, Laurent (2008). “A Multi-Points Criterion for Deterministic Parallel Global Optimization Based on Gaussian Processes.”
Wang, Jialei, Clark, C. S, Liu, Eric, Frazier, I. P (2020). “Parallel Bayesian Global Optimization of Expensive Functions.” Operations Research, 68(6), 1850--1865.

Examples

Run this code

# \donttest{
if (requireNamespace("mlr3learners") &
    requireNamespace("DiceKriging") &
    requireNamespace("rgenoud")) {

  library(bbotk)
  library(paradox)
  library(mlr3learners)

  fun = function(xs) {
    list(y = xs$x ^ 2)
  }
  domain = ps(x = p_dbl(lower = -10, upper = 10))
  codomain = ps(y = p_dbl(tags = "minimize"))
  objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)

  instance = OptimInstanceBatchSingleCrit$new(
    objective = objective,
    terminator = trm("evals", n_evals = 7))

  surrogate = default_surrogate(instance)

  acq_function = acqf("ei")

  acq_optimizer = acqo(
    optimizer = opt("random_search", batch_size = 100),
    terminator = trm("evals", n_evals = 100))

  optimizer = opt("mbo",
    loop_function = bayesopt_mpcl,
    surrogate = surrogate,
    acq_function = acq_function,
    acq_optimizer = acq_optimizer,
    args = list(q = 3))

  optimizer$optimize(instance)
}
# }