Optim.jl OnceDifferentiable Baby Steps

[mohamed82008]

@iwelch

myfungradients(x)= [ 2*(1 + (x[2]-3)^2)(x[1]-2 ), 2(1 + (x[1]-2)^2)(x[2]-3) ]
myfunhessian(x)= [ [ 2 + 2(x[2]-3)^2, 4*(x[1]-2)(x[2]-3) ], [ 4(x[1]-2)(x[2]-3) , 2 + 2(x[2]-2)^2 ] ]

This is not a recommended style since it allocates memory for the output.

@iwelch

function g!(G,x) G=myfungradients(x) end#function
function fg!(G,x) g!(G,x); f(x) end#function (both! can be used to shorten calculations)

The idea of g! is that it should modify the object that G refers to in-place. What you are doing here is simply rebinding the local G name to the array output by myfungradients(x), but outside the function, the passed object was not modified. See this for more details Values vs. Bindings: The Map is Not the Territory · John Myles White. When you modify the object that G binds to in-place, you are not allocating additional memory so that’s efficient.

@iwelch

optimize( td, z2, BFGS()) ## works

optimize( od, z2, BFGS()) ## error

I am actually surprised any of them works at all!

See these 2 examples:

1. Conjugate Gradient - Optim.jl
2. http://julianlsolvers.github.io/Optim.jl/stable/algo/autodiff/