I am not sure if this is intentional or not, but all the optimisation algorithms exit with an error if the initial guess was exactly at the optimum. The simplest example:
using OptimKit; alg=GradientDescent(;); fg(x)=sum(x.^2), 2.0 .*x; optimize(fg, [0.],alg);
This code terminates with an error "linesearch was not given a descent direction!". The same for the conjugate gradient algorithm. The LBFGS algorithm tries to start the linear search with d $d\phi_0=NaN$ and stalls without an error message.
I am not sure if this is intentional or not, but all the optimisation algorithms exit with an error if the initial guess was exactly at the optimum. The simplest example:
$d\phi_0=NaN$ and stalls without an error message.
using OptimKit; alg=GradientDescent(;); fg(x)=sum(x.^2), 2.0 .*x; optimize(fg, [0.],alg);This code terminates with an error "linesearch was not given a descent direction!". The same for the conjugate gradient algorithm. The LBFGS algorithm tries to start the linear search with d