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Optimize.jl

Optimization methods implemented in Julia. Architecture heavily inspired by the Optim.jl package.

Currently 5 direct search methods are implemented:

Installation

This package is not published, since it is a learning project and doesn't provide anything that doesn't already exist in the plethora of Julia packages. To use these methods, clone the git repository:

> git clone git@github.com:slindberg/Optimize.jl.git

Currently the library's only dependency is the Optim package, used for line search. In the Juila REPL:

> Pkg.add("Optim")

Usage

The only public method is optimize which accepts two structs:

function optimize(method::Method, problem::Problem)

The Problem struct defines the optimization problem:

# Initial coordinate, defines the problem's dimensionality
x_0 = [0.0,0.0]

# The problem's objective function, accepts a single indexable
# object and returns the objective's value at that coordinate
f(x) = (x[2] - x[1]^2)^2 + (x[1] - 4)^2

problem = Problem(f, x_0)

The Method struct identifies the method to use to solve the optimization problem. Each method has a set of configuration arguments:

  • Cyclic Coordinate Search

    method = CyclicCoordinateSearch(
  use_acceleration = false        # Whether or not to use an 'acceleration' direction
)

  • Hooke and Jeeves

    method = HookeAndJeeves(
  initial_step_size = 0.5,        # Initial distance to travel in each coordinate direction
  step_reduction = 0.5,           # Factor by which to reduce the step size
  ϵ_h = 1e-8                      # Minimum step size, used to determine convergence
)

  • Rosenbrock's Method

    method = Rosenbrock(
  initial_step_size = 0.5,        # Initial distance to travel in each direction
  forward_step_multiplier = 5.0,  # Step size expansion factor, should be > 1
  backward_step_multiplier = 0.5, # Step size reduction factor, should be > 0 and < 1
  ϵ_h = 1e-8                      # Minimum step size, used to determine convergence
)

  • Random Hill Climbing

    method = RandomHillClimbing(
  step_sizes = [ 0.1 ],           # Distances to neighboring coordinates
  max_failed_neighbors = 2*n*s-1  # Max number of unimproved neighbors before search is stopped
)

  • Exhaustive Search

    method = ExhaustiveSearch(
  search_space = 0:0.1:1          # Single range or array of ranges defining search grid
)

An optional argument to optimize provides global search options:

function optimize(method::Method, problem::Problem, options::Options)

The Options struct takes

options = Options(
  ϵ_f = 1e-16,            # Objective function convergence criteria
  ϵ_x = 1e-16,            # Minimizer convergence criteria
  max_iterations = 1000,  # Maximum number of iterations before search is stopped
  callback = nothing      # Optional callback that is invoked every search iteration
)

The optional callback is invoked with three parameters:

function callback(iteration, x_current, f_current)
  # ...
end

Examples

The examples/ directory has examples showing how to use this package and plot results. Plotting uses the Plots package, which you will need to install in addition to whichever plotting backend you choose.

Tests

Nope, this is a quick and dirty learning project.