RcppTrust is a thread-safe C++ port of the trust-region optimizer in Charles J. Geyer’s CRAN package trust, built for use in nlmixr2est. It implements the exact same algorithm – same Newton / easy-easy / hard-easy / hard-hard trust-region subproblem, same termination criteria – and exposes it three ways:
-
trust(), a drop-in R replacement fortrust::trust(), with the same arguments, defaults, and return value. -
trust_solve_c(), a thread-safe C entry point (a C objective function pointer plus a small options struct, no R API calls) safe to call from parallel C++ code such as an OpenMP loop. - A header-only, positionally-indexed function-pointer table (
inst/include/RcppTrust.h), so another package can calltrust_solve_c()without linking against this package’s shared library – the same patternrxode2/n1qn1/lbfgsb3cuse across the nlmixr2 ecosystem.
See vignette("RcppTrust") for what’s the same as upstream trust, what’s different, and a worked example of the C interface and the registration pattern.
Note this package was generated with the help of AI (Claude/Gemini).
Installation
You can install the development version of RcppTrust from GitHub with:
# install.packages("pak")
pak::pak("nlmixr2/RcppTrust")Example
trust() is a straight substitute for trust::trust():
library(RcppTrust)
# Rosenbrock's function, the example from ?trust::trust
objfun <- function(x) {
f <- expression(100 * (x2 - x1^2)^2 + (1 - x1)^2)
g1 <- D(f, "x1"); g2 <- D(f, "x2")
h11 <- D(g1, "x1"); h12 <- D(g1, "x2"); h22 <- D(g2, "x2")
x1 <- x[1]; x2 <- x[2]
list(
value = eval(f), gradient = c(eval(g1), eval(g2)),
hessian = rbind(c(eval(h11), eval(h12)), c(eval(h12), eval(h22)))
)
}
out <- trust(objfun, c(3, 1), 1, 5)
out[c("value", "argument", "converged", "iterations")]
#> $value
#> [1] 5.165437e-15
#>
#> $argument
#> [1] 1 1
#>
#> $converged
#> [1] TRUE
#>
#> $iterations
#> [1] 21