Optim line search

Tip

This example is also available as a Jupyter notebook: optim_linesearch.ipynb

This example shows how to use LineSearches with Optim. We solve the Rosenbrock problem with two different line search algorithms.

First, run Newton with the default line search algorithm:

using Optim, LineSearches
import OptimTestProblems.MultivariateProblems
UP = MultivariateProblems.UnconstrainedProblems
prob = UP.examples["Rosenbrock"]

algo_hz = Newton(linesearch = HagerZhang())
res_hz = Optim.optimize(prob.f, prob.g!, prob.h!, prob.initial_x, method=algo_hz)

Now we can try Newton with the cubic backtracking line search, which reduced the number of objective and gradient calls.

algo_bt3 = Newton(linesearch = BackTracking(order=3))
res_bt3 = Optim.optimize(prob.f, prob.g!, prob.h!, prob.initial_x, method=algo_bt3)

This page was generated using Literate.jl.