I have an unconstrained minimization problem of many variables for which I know the gradient exactly. I turned to the conjugate gradient method contained in
scipy.optimize.minimize (which uses the Polak-Ribiere algorithm), but it throws a
LineSearchError when I try to converge the algorithm beyond the square root of machine precision. This seems to be a common occurence with a certain class of line-search algorithms.
The square root of machine precision is not enough for my purposes. Is there a robust algorithm available that uses an approximate line-search, or something similar, which does enable one to converge to machine precision?