Discrete choice modeling and estimation in Julia.

• Gradient descent.
• Newton.
• Quasi-Newton using inverse BFGS.
• Trust region using exact Hessian, BFGS or SR1.

(v1.0) pkg> add https://github.com/brilhana/DiscreteChoice.jl

The function, and its gradient and Hessian (depending on the method) must be supplied to the solver by the user. An easy way to do it is to use ForwardDiff.

using DiscreteChoice
using ForwardDiff

f(x) = (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2;

g(x) = ForwardDiff.gradient(f, x);

H(x) = ForwardDiff.hessian(f, x);

# Trust region using Steihaug-Toint.
optimize(f, g, H, tcg, zeros(2))

• Logit