Hi Oliver,
The result of optimize is an object with a field called minimum that has the
solution.
Try something like the following:
julia> res = optimize(x -> (10.0 - x[1])^2, [0.0], method = :gradient_descent)
Results of Optimization Algorithm
* Algorithm: Gradient Descent
* Starting Point: 0
* Minimum: 10.000000000118629
* Value of Function at Minimum: 0.000000
* Iterations: 1
* Convergence: true
* |x - x'| < 1.0e-32: false
* |f(x) - f(x')| / |f(x)| < 1.0e-08: false
* |g(x)| < 1.0e-08: true
* Exceeded Maximum Number of Iterations: false
* Objective Function Calls: 4
* Gradient Call: 4
julia> res.minimum
1-element Array{Float64,1}:
10.0
— John
On Jan 4, 2014, at 10:52 AM, Oliver Lylloff <[email protected]> wrote:
> Hello all,
>
> I'm trying to get acquainted with the Optim package - so far I think
> everything looks very interesting.
> I would like to get the solution vector of each iteration as an output (e.g.
> for plotting) - how do I do that?
>
> Trying the Rosenbrock example from https://github.com/JuliaOpt/Optim.jl with
> extended_trace option
>
> optimize(f,g!,[0.0,0.0],method=:gradient_descent,extended_trace=true)
>
> prints the solution vector x at each iteration but I can't seem to access it
> and store it. Any ideas?
>
>
> Best,
> Oliver
>
>
