Expose Ipopt's new_x argument about ipopt.jl HOT 6 CLOSED

This is easy and cheap to emulate by storing the previous input vector and checking if it's equal to the new one. We'd accept a PR for this, but anyway I'd recommend coding to the MathProgBase interface which would let you easily switch between solvers (Ipopt, KNITRO, NLopt).

from ipopt.jl.

Ok, thank you, I'll look into MathProgBase. For my own understanding, how would you store the previous value? I can't seem to access variables outside the scope of eval functions. (An example is below.)

using Ipopt

prev_x = zeros(2)
prev_v = 0.0
function eval_f(x::Vector{Float64})
  if (x == prev_x)
    return prev_v
  else
    prev_x = x
    prev_v = x[1]^2 + x[2]^2
  end
  prev_v
end

function eval_grad_f(x::Vector{Float64}, grad_f::Vector{Float64})
  grad_f[1] = 2 * x[1]
  grad_f[2] = 2 * x[2]
end

function intermediate(alg_mod::Int, iter_count::Int, 
  obj_value::Float64, inf_pr::Float64, inf_du::Float64, mu::Float64,
  d_norm::Float64, regularization_size::Float64, alpha_du::Float64, alpha_pr::Float64, 
  ls_trials::Int)
  println("Iteration $iter_count, objective value is $obj_value.")
  return true
end

n = 2
x_L = [1.0, 1.0]
x_U = [2.0, 2.0]

function eval_no_jac_g(x, mode, rows, cols, values)
end

function eval_no_g(x, g)
end

prob = createProblem(n, x_L, x_U, 0, Array(Float64, 0), Array(Float64, 0), 0, 2,
                     eval_f, eval_no_g, eval_grad_f, eval_no_jac_g)
addOption(prob, "hessian_approximation", "limited-memory")

prob.x = [1.5, 2.5]
stat = solveProblem(prob)
# Gives the error:
# ERROR: prev_x not defined
#  in eval_f at none:2
#  in eval_f_wrapper at /home/rgiordan/.julia/v0.3/Ipopt/src/Ipopt.jl:86

from ipopt.jl.

Try prev_x[:] = x[:], that way it replaces the "global" prev_x instead of rebinding the name.

from ipopt.jl.

If you look in the source you'll see that I did wrap the C interface, including new_x, already. However, I punted on exposing it to user-facing code because I didn't know how I wanted it to look in Julia and I didn't have a need for it.

from ipopt.jl.

Thanks, using the brackets to refer to the global prev_x works fine. Unless I'm mistaken, using some sort of similar global variable trick will also be necessary with MathProgBase.

Yes, I noticed that you had new_x in the C interface but hadn't exposed it to Julia.

from ipopt.jl.

It's not very well documented at this point, but you wouldn't use globals with MathProgBase, you would store the vector inside your NLPEvaluator object, so there's no type inference/performance issues to worry about. See the discussion here: JuliaNLSolvers/Optim.jl#107

from ipopt.jl.