[SciPy-Dev] optimize ncg approx_fhess_p bug?

[josef.pktd@gmai...]

is this a bug?

why is there a  `*p` in the approx_fhess

def approx_fhess_p(x0,p,fprime,epsilon,*args):
    f2 = fprime(*((x0+epsilon*p,)+args))
    f1 = fprime(*((x0,)+args))
    return (f2 - f1)/epsilon

from a quick search this function is only used in fmin_ncg, which has
convergence problem if epsilon is small.
convergence to solution looks ok when I set epsilon to 1e-4 or 1e-6.

but with predefined epsilon the step in the approx hessian calculation
looks to small:

>>> t.grad(t.solution)*_epsilon
array([  0.00000000e+00,  -7.71175966e-16,  -6.94483304e-15])

non-convergence was a test failure reported by Alan with scipy 0.7.2

I'm not sure about this because I never looked at the optimize code
before, and I only did some quick checking.

dropping the p in the approx fhess, I get a very fast solution
(full_output) of the test problem in optimize/tests/optimize.py
(array([  1.46693016e-07,  -5.24869440e-01,   4.87527692e-01]), 1.55913216727411
23, 5, 18, 0, 0)

Any ideas?

Josef