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#' Adjiman function
#'
#' This two-dimensional multimodal test function follows the formula
#' \deqn{f(\mathbf{x}) = \cos(\mathbf{x}_1)\sin(\mathbf{x}_2) - \frac{\mathbf{x}_1}{(\mathbf{x}_2^2 + 1)}}
#' with \eqn{\mathbf{x}_1 \in [-1, 2], \mathbf{x}_2 \in [2, 1]}.
#'
#' @references C. S. Adjiman, S. Sallwig, C. A. Flouda, A. Neumaier, A Global Optimization
#' Method, aBB for General Twice-Differentiable NLPs-1, Theoretical Advances, Computers
#' Chemical Engineering, vol. 22, no. 9, pp. 1137-1158, 1998.
#'
#' @template ret_smoof_single
#' @export
makeAdjimanFunction = function() {
makeSingleObjectiveFunction(
name = "Adjiman Function",
id = "adjiman_2d",
fn = function(x) {
assertNumeric(x, len = 2L, any.missing = FALSE, all.missing = FALSE)
cos(x[1]) * sin(x[2]) - x[1] / (x[2]^2 + 1)
},
par.set = makeNumericParamSet(
len = 2L,
id = "x",
lower = c(-1, -1),
upper = c(2, 1),
vector = TRUE
),
tags = attr(makeAdjimanFunction, "tags"),
global.opt.params = c(2, 0.10578),
global.opt.value = -2.02181
)
}
class(makeAdjimanFunction) = c("function", "smoof_generator")
attr(makeAdjimanFunction, "name") = c("Adjiman")
attr(makeAdjimanFunction, "type") = c("single-objective")
attr(makeAdjimanFunction, "tags") = c("single-objective", "continuous", "differentiable", "non-separable", "non-scalable", "multimodal")
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