Optimize arbitrary and differentiable functions

The function compiles the code to C++ and uses Armadillo and ensmallen to optimize it.

compile_optimization_problem(data = list(), evaluate, gradient,
  optimizer = optimizer_SA())

Arguments

data	a named list of prior data you would like to supply to the evaluate function.
evaluate	a function that is to be minimized. It should return a single numeric.
gradient	optional, a function computing the gradient of evaluate
optimizer	one of the many optimizers

Examples

if (FALSE) {
optimize <- compile_optimization_problem(
  data = list(),
  evaluate = function(x) {
    return(2 * norm(x)^2)
  },
  optimizer = optimizer_SA()
)

# should be roughly c(0, 0, 0)
result <- optimize(matrix(c(1, -1, 1), ncol = 1))
}