Optim.jl

Univariate and multivariate optimization in Julia.

Optim.jl is part of the JuliaNLSolvers family.

Help and support

For help and support, please post on the Optimization (Mathematical) section of the Julia discourse or the #math-optimization channel of the Julia slack.

Installation

Install Optim.jl using the Julia package manager:

import Pkg
Pkg.add("Optim")

Documentation

The online documentation is available at https://julianlsolvers.github.io/Optim.jl/stable.

Example

To minimize the Rosenbrock function, do:

julia> using Optim

julia> rosenbrock(x) =  (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2
rosenbrock (generic function with 1 method)

julia> result = optimize(rosenbrock, zeros(2), BFGS())
 * Status: success

 * Candidate solution
    Final objective value:     5.471433e-17

 * Found with
    Algorithm:     BFGS

 * Convergence measures
    |x - x'|               = 3.47e-07 ≰ 0.0e+00
    |x - x'|/|x'|          = 3.47e-07 ≰ 0.0e+00
    |f(x) - f(x')|         = 6.59e-14 ≰ 0.0e+00
    |f(x) - f(x')|/|f(x')| = 1.20e+03 ≰ 0.0e+00
    |g(x)|                 = 2.33e-09 ≤ 1.0e-08

 * Work counters
    Seconds run:   0  (vs limit Inf)
    Iterations:    16
    f(x) calls:    53
    ∇f(x) calls:   53

julia> Optim.minimizer(result)
2-element Vector{Float64}:
 0.9999999926033423
 0.9999999852005355

julia> Optim.minimum(result)
5.471432670590216e-17

To get information on the keywords used to construct method instances, use the Julia REPL help prompt (?)

help?> LBFGS
search: LBFGS

  LBFGS
  ≡≡≡≡≡

  Constructor
  ===========

  LBFGS(; m::Integer = 10,
  alphaguess = LineSearches.InitialStatic(),
  linesearch = LineSearches.HagerZhang(),
  P=nothing,
  precondprep = (P, x) -> nothing,
  manifold = Flat(),
  scaleinvH0::Bool = true && (typeof(P) <: Nothing))

  LBFGS has two special keywords; the memory length m, and the scaleinvH0 flag.
  The memory length determines how many previous Hessian approximations to
  store. When scaleinvH0 == true, then the initial guess in the two-loop
  recursion to approximate the inverse Hessian is the scaled identity, as can be
  found in Nocedal and Wright (2nd edition) (sec. 7.2).

  In addition, LBFGS supports preconditioning via the P and precondprep keywords.

  Description
  ===========

  The LBFGS method implements the limited-memory BFGS algorithm as described in
  Nocedal and Wright (sec. 7.2, 2006) and original paper by Liu & Nocedal
  (1989). It is a quasi-Newton method that updates an approximation to the
  Hessian using past approximations as well as the gradient.

  References
  ==========

    •  Wright, S. J. and J. Nocedal (2006), Numerical optimization, 2nd edition.
       Springer

    •  Liu, D. C. and Nocedal, J. (1989). "On the Limited Memory Method for
       Large Scale Optimization". Mathematical Programming B. 45 (3): 503–528

Use with JuMP

You can use Optim.jl with JuMP.jl as follows:

julia> using JuMP, Optim

julia> model = Model(Optim.Optimizer);

julia> set_optimizer_attribute(model, "method", BFGS())

julia> @variable(model, x[1:2]);

julia> @objective(model, Min, (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2)
(x[1]² - 2 x[1] + 1) + (100.0 * ((-x[1]² + x[2]) ^ 2.0))

julia> optimize!(model)

julia> objective_value(model)
3.7218241804173566e-21

julia> value.(x)
2-element Vector{Float64}:
 0.9999999999373603
 0.99999999986862

Citation

If you use Optim.jl in your work, please cite the following:

@article{mogensen2018optim,
  author  = {Mogensen, Patrick Kofod and Riseth, Asbj{\o}rn Nilsen},
  title   = {Optim: A mathematical optimization package for {Julia}},
  journal = {Journal of Open Source Software},
  year    = {2018},
  volume  = {3},
  number  = {24},
  pages   = {615},
  doi     = {10.21105/joss.00615}
}