Random Search Solver

Creates a solver that evaluates the log-likelihood at random points and returns the best. Useful for high-dimensional problems where grid search is infeasible.

Usage

random_search(sampler, n = 100L)

Arguments

sampler

Function generating random parameter vectors

n

Number of random points to evaluate

Value

A solver function

Details

Unlike grid search, random search scales better to high dimensions. The sampler should generate points in a reasonable region; points outside the problem's constraint support are skipped.

Examples

# Create a random search solver with uniform sampling
solver <- random_search(
  sampler = uniform_sampler(c(-10, 0.1), c(10, 5)),
  n = 100
)