Optimization

Local Nonlinear Optimization with Optim.jl

One of the core libraries for nonlinear optimization is Optim.jl. Optim.jl is a lot like the standard optimizers you'd find in SciPy or MATLAB. You give it a function and it finds the minimum. For example, if you give it a univariate function it uses Brent's method to find the minimum in an interval:



In [6]:

    
using Optim
f(x) = sin(x)+cos(x)
Optim.optimize(f,0.0,2π) # Find a minimum between 0 and 2π









    Out[6]:





Results of Optimization Algorithm
 * Algorithm: Brent's Method
 * Search Interval: [0.000000, 6.283185]
 * Minimizer: 3.926991e+00
 * Minimum: -1.414214e+00
 * Iterations: 11
 * Convergence: max(|x - x_upper|, |x - x_lower|) <= 2*(1.5e-08*|x|+2.2e-16): true
 * Objective Function Calls: 12

If you give it a function which requires vector input with scalar output, it will give the vector local minima:



In [7]:

    
f(x) = sin(x[1])+cos(x[1]+x[2])
Optim.optimize(f,zeros(2)) # Find a minimum starting at [0.0,0.0]









    Out[7]:





Results of Optimization Algorithm
 * Algorithm: Nelder-Mead
 * Starting Point: [0.0,0.0]
 * Minimizer: [-1.570684758073873,-1.5708688186478836]
 * Minimum: -2.000000e+00
 * Iterations: 49
 * Convergence: true
   *  √(Σ(yᵢ-ȳ)²)/n < 1.0e-08: true
   * Reached Maximum Number of Iterations: false
 * Objective Calls: 95

You can refer to Optim's large library of methods and pass in a method choice to have different properties. Let's choose BFGS:



In [8]:

    
Optim.optimize(f,zeros(2),BFGS())









    Out[8]:





Results of Optimization Algorithm
 * Algorithm: BFGS
 * Starting Point: [0.0,0.0]
 * Minimizer: [-1.5707963314270867,-1.5707963181008544]
 * Minimum: -2.000000e+00
 * Iterations: 6
 * Convergence: true
   * |x - x'| ≤ 0.0e+00: false 
     |x - x'| = 4.10e-04 
   * |f(x) - f(x')| ≤ 0.0e+00 |f(x)|: false
     |f(x) - f(x')| = -9.67e-08 |f(x)|
   * |g(x)| ≤ 1.0e-08: true 
     |g(x)| = 4.06e-09 
   * Stopped by an increasing objective: false
   * Reached Maximum Number of Iterations: false
 * Objective Calls: 15
 * Gradient Calls: 15

Global Nonlinear Optimization with BlackBoxOptim.jl

Global optimization is provided with a native Julia implementation at BlackBoxOptim.jl. You have to give it box constraints and tell it the size of the input vector:



In [9]:

    
using BlackBoxOptim

function rosenbrock2d(x)
  return (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2
end
res = bboptimize(rosenbrock2d; SearchRange = (-5.0, 5.0), NumDimensions = 2)









    



Starting optimization with optimizer DiffEvoOpt{FitPopulation{Float64},RadiusLimitedSelector,BlackBoxOptim.AdaptiveDiffEvoRandBin{3},RandomBound{RangePerDimSearchSpace}}
0.00 secs, 0 evals, 0 steps

Optimization stopped after 10001 steps and 0.013683080673217773 seconds
Termination reason: Max number of steps (10000) reached
Steps per second = 730902.6555383248
Function evals per second = 738722.5319649422
Improvements/step = 0.2172
Total function evaluations = 10108


Best candidate found: [1.0, 1.0]

Fitness: 0.000000000







    Out[9]:





BlackBoxOptim.OptimizationResults("adaptive_de_rand_1_bin_radiuslimited", "Max number of steps (10000) reached", 10001, 1.536689117938819e9, 0.013683080673217773, DictChain{Symbol,Any}[DictChain{Symbol,Any}[Dict{Symbol,Any}(:RngSeed=>189964,:NumDimensions=>2,:SearchRange=>(-5.0, 5.0),:MaxSteps=>10000),Dict{Symbol,Any}()],Dict{Symbol,Any}(:FitnessScheme=>ScalarFitnessScheme{true}(),:NumDimensions=>:NotSpecified,:PopulationSize=>50,:MaxTime=>0.0,:SearchRange=>(-1.0, 1.0),:Method=>:adaptive_de_rand_1_bin_radiuslimited,:MaxNumStepsWithoutFuncEvals=>100,:RngSeed=>1234,:MaxFuncEvals=>0,:SaveTrace=>false…)], 10108, ScalarFitnessScheme{true}(), BlackBoxOptim.TopListArchiveOutput{Float64,Array{Float64,1}}(7.794981123521699e-27, [1.0, 1.0]), BlackBoxOptim.PopulationOptimizerOutput{FitPopulation{Float64}}(FitPopulation{Float64}([1.0 1.0 … 1.0 1.0; 1.0 1.0 … 1.0 1.0], NaN, [1.27603e-23, 3.92402e-23, 1.60016e-23, 1.61726e-23, 2.00834e-23, 6.01524e-23, 2.16747e-22, 4.2942e-23, 3.83008e-23, 8.03423e-23  …  6.35954e-25, 1.22529e-24, 1.60825e-25, 9.94304e-25, 1.41704e-24, 6.42558e-24, 3.93374e-24, 8.25185e-24, 1.89228e-23, 1.44361e-23], 0, BlackBoxOptim.Candidate{Float64}[Candidate{Float64}([1.0, 1.0], 10, 8.03423e-23, AdaptiveDiffEvoRandBin{3}(AdaptiveDiffEvoParameters(BimodalCauchy(Distributions.Cauchy{Float64}(μ=0.65, σ=0.1), Distributions.Cauchy{Float64}(μ=1.0, σ=0.1), 0.5, false, true), BimodalCauchy(Distributions.Cauchy{Float64}(μ=0.1, σ=0.1), Distributions.Cauchy{Float64}(μ=0.95, σ=0.1), 0.5, false, true), [0.697891, 0.720263, 0.88478, 0.809857, 0.434573, 0.280839, 1.0, 0.653261, 0.990107, 0.54517  …  0.829696, 0.680993, 0.96154, 1.0, 0.917572, 1.0, 0.835965, 1.0, 0.996137, 0.999622], [0.830166, 0.160064, 0.153951, 1.0, 0.78871, 0.256191, 0.139636, 1.0, 0.986488, 0.229464  …  0.932861, 0.0857158, 0.0022386, 0.595109, 0.980229, 0.122564, 0.39074, 1.0, 0.848218, 0.913758])), 0), Candidate{Float64}([1.0, 1.0], 10, 8.63425e-22, AdaptiveDiffEvoRandBin{3}(AdaptiveDiffEvoParameters(BimodalCauchy(Distributions.Cauchy{Float64}(μ=0.65, σ=0.1), Distributions.Cauchy{Float64}(μ=1.0, σ=0.1), 0.5, false, true), BimodalCauchy(Distributions.Cauchy{Float64}(μ=0.1, σ=0.1), Distributions.Cauchy{Float64}(μ=0.95, σ=0.1), 0.5, false, true), [0.697891, 0.720263, 0.88478, 0.809857, 0.434573, 0.280839, 1.0, 0.653261, 0.990107, 0.54517  …  0.829696, 0.680993, 0.96154, 1.0, 0.917572, 1.0, 0.835965, 1.0, 0.996137, 0.999622], [0.830166, 0.160064, 0.153951, 1.0, 0.78871, 0.256191, 0.139636, 1.0, 0.986488, 0.229464  …  0.932861, 0.0857158, 0.0022386, 0.595109, 0.980229, 0.122564, 0.39074, 1.0, 0.848218, 0.913758])), 0)])))

JuMP, Convex.jl, NLopt.jl

JuMP.jl is a large library for all sorts of optimization problems. It has solvers for linear, quadratic, etc. programming problems. If you're not doing nonlinear optimization JuMP is a great choice. If you're looking to do convex programming, Convex.jl is a library with methods specific for this purpose. If you want to do nonlinear optimization with constraints, NLopt.jl is a library with a large set of choices. It also has a bunch of derivative-free local optimization methods. It's only issue is that its an interface to a C library and can be more difficult to debug than the native Julia codes, but otherwise it's a great alternative to Optim and BlackBoxOptim.

Problem 1

Use Optim.jl to optimize Hosaki's Function. Use the initial condition [2.0,2.0].

Problem 2

BlackBoxOptim.jl to find global minima of the Adjiman Function with $-1 < x_1 < 2$ and $-1 < x_2 < 1$.