NLOpt

Idea is to provide wrappers for most performant opensource optimization tools that are available. This shard is about nonlinear optimization.

According to http://plato.asu.edu/bench.html IPOPT performs pretty fast, almost on par with commercial solvers. According to http://repositorium.sdum.uminho.pt/bitstream/1822/39109/1/Eliana_ICNAAM2014.pdf NLopt is slower, but still finds optimum.

The problem is that IPOPT is pretty complex to get installed, have a lot of dependencies, so it perhaps doesn't fit in the general-purpose scientific library. NLopt has also much simpler API.

So this is a wrapper of NLopt library.

Installation

Install NLopt from package manager (apt install libnlopt0 for Ubuntu, https://aur.archlinux.org/packages/nlopt for Arch).
Add this to your application's shard.yml:

dependencies:
  nlopt:
    github: konovod/nlopt

On Windows: you can get compiled version 2.4.2 using instructions from this page: http://ab-initio.mit.edu/wiki/index.php?title=NLopt_on_Windows Basically

download http://ab-initio.mit.edu/nlopt/nlopt-2.4.2-dll64.zip
unpack, rename libnlopt-0.def to nlopt.def, call lib /def:nlopt.def /machine:x64 in the unpacked directory to generate nlopt.lib
copy libnlopt-0.dll to your program directory (rename to nlopt.dll)
copy nlopt.lib to where your Crystal looks for lib files

Alternatively, you can compile latest (2.7.1) version from source (https://github.com/stevengj/nlopt). Windows CI Action does it too, so you can grab dll library from it's artifacts. Note that it depends on msvcp140.dll.

Usage

require "nlopt"

you can check spec directory for simple examples. Supported features:

[x] creating and freeing solvers with any of 43 supported algorithms

s1 = NLopt::Solver.new(NLopt::Algorithm::LnCobyla, 100)
s1.dimension.should eq 100
(s1.algorithm.to_s[0..5]).should eq "COBYLA"
s2 = s1.clone
s1.free
expect_raises(Exception) do
  s1.algorithm
end
s2.algorithm.should eq NLopt::Algorithm::LnCobyla

[x] simple optimization of given function

s1 = NLopt::Solver.new(NLopt::Algorithm::LnCobyla, 2)
s1.objective = ->(x : Slice(Float64)) { (x[0] - 3)**2 + (x[1] - 2)**2 }
res, x, f = s1.solve
res.should eq NLopt::Result::XtolReached
x[0].should be_close(3, 1e-7)
x[1].should be_close(2, 1e-7)
f.should be_close(0, 1e-7)

[x] setting parameters of solver

s1 = NLopt::Solver.new(NLopt::Algorithm::LnCobyla, 100)
s1.optim_dir = NLopt::Direction::Maximize # default is minimize
# stopval, ftol_rel, ftol_abs, xtol_rel, maxeval, maxtime, population, vector_storage are supported. Check nlopt documentation for description
s1.stopval = -1e6
# local_optimizer for algorithms that support it
s1 = NLopt::Solver.new(NLopt::Algorithm::Auglag, 100)
s1_local = NLopt::Solver.new(NLopt::Algorithm::LnCobyla, 100)
s1.local_optimizer(s1_local)

[x] setting minimum and maximum bound, initial guess and initial step for variables

s1 = NLopt::Solver.new(NLopt::Algorithm::LnCobyla, 2)
s1.variables[0].min = 2.0
s1.variables[1].max = 10.0
# or several params at once:
s1.variables[0].set(min: 4, guess: 50)
s1.variables[1].set(max: 2, initial_step: 1.0)

[x] use separate function for gradient calculation on calculate gradient together with objective function

s1 = NLopt::Solver.new(NLopt::Algorithm::LdMma, 2)
s1.objective = ->(x : Slice(Float64)) { (x[0] - 1) * (x[1] - 2)**2 }
s1.obj_gradient = ->(x : Slice(Float64), grad : Slice(Float64)) do
  grad[0] = (x[1] - 2)**2
  grad[1] = 2*(x[0] - 1)*(x[1] - 2)
end
# or:
s1 = NLopt::Solver.new(NLopt::Algorithm::LdMma, 2)
s1.objective = ->(x : Slice(Float64), grad : Slice(Float64)?) do
  if grad
    grad[0] = (x[1] - 2)**2
    grad[1] = 2*(x[0] - 1)*(x[1] - 2)
  end
  (x[0] - 1) * (x[1] - 2)**2
end

[x] Constraints in forms of equalities and inequalities

s1 = NLopt::Solver.new(NLopt::Algorithm::LdMma, 2)
s1.xtol_rel = 1e-4
s1.objective = ->(x : Slice(Float64), grad : Slice(Float64)?) do
  if grad
    grad[0] = 0.0
    grad[1] = 0.5 / Math.sqrt(x[1])
  end
  Math.sqrt(x[1])
end
a = 2
b = 0
s1.constraints << NLopt.inequality do |x, grad|
  if grad
    grad[0] = 3 * a * (a*x[0] + b) * (a*x[0] + b)
    grad[1] = -1.0
  end
  ((a*x[0] + b) * (a*x[0] + b) * (a*x[0] + b) - x[1])
end
s1.constraints << NLopt.equality do |x, grad|
  if grad
    grad[0] = 1.0
    grad[1] = 1.0
  end
  x[0] + x[1] - 3
end

[x] Vector-valued constraints

s1.constraints << NLopt.inequalities(2) do |x, grad, result|
  if grad
    grad[0] = 3 * a1 * (a1*x[0] + b1) * (a1*x[0] + b1)
    grad[1] = -1.0
    grad[2] = 3 * a2 * (a2*x[0] + b2) * (a2*x[0] + b2)
    grad[3] = -1.0
  end
  result[0] = ((a1*x[0] + b1) * (a1*x[0] + b1) * (a1*x[0] + b1) - x[1])
  result[1] = ((a2*x[0] + b2) * (a2*x[0] + b2) * (a2*x[0] + b2) - x[1])
end

[ ] Forced termination (would require multithreading as currently optimization is syncronous)
[ ] Algorithm-specific parameters (only for NLOpt version >= 2.7)
[x] setting random seed

NLopt.srand # will randomize seed
NLopt.srand(12345_u64) # will set fixed seed

[x] Preconditioning with approximate Hessians for objective function

    s1.precondition = ->(x : Slice(Float64), hessian : Slice(Float64)) do
     hessian[0] = 1.0
     hessian[1] = 0.0
     hessian[2] = 1.0
     hessian[3] = 0.0
   end

   # or, in a more effecient way

    s1.precondition = ->(x : Slice(Float64), v : Slice(Float64), vpre : Slice(Float64)) do
     vpre[0] = v[0]
     vpre[1] = v[1]
   end

[ ] Preconditioning with approximate Hessians for constraints
[x] Get number of objective function evaluations (Solver#num_evals) to estimate efficiency
[x] Version number

LibNLopt.version(out major, out minor, out bugfix)
puts "NLopt Version: #{major}.#{minor}.#{bugfix}"

Contributing

Fork it ( https://github.com/konovod/nlopt.cr/fork )
Create your feature branch (git checkout -b my-new-feature)
Commit your changes (git commit -am 'Add some feature')
Push to the branch (git push origin my-new-feature)
Create a new Pull Request

CrystalDoc.info

nlopt

NLOpt

Installation

Usage

Contributing