Local Derivative-Free Optimization

Derivative-free optimizers are optimizers that can be used even in cases where no derivatives or automatic differentiation is specified. While they tend to be less efficient than derivative-based optimizers, they can be easily applied to cases where defining derivatives is difficult.

NLOpt COBYLA

Optim.jl

  • Optim.NelderMead: Nelder-Mead optimizer

    • solve(problem, NelderMead(parameters, initial_simplex))
    • parameters = AdaptiveParameters() or parameters = FixedParameters()
    • initial_simplex = AffineSimplexer()
    • defaults to: parameters = AdaptiveParameters(), initial_simplex = AffineSimplexer()

  • Optim.SimulatedAnnealing: Simulated Annealing

    • solve(problem, SimulatedAnnealing(neighbor, T, p))
    • neighbor is a mutating function of the current and proposed x
    • T is a function of the current iteration that returns a temperature
    • p is a function of the current temperature
    • defaults to: neighbor = default_neighbor!, T = default_temperature, p = kirkpatrick

NLopt.jl