# algorithms.optimize¶

## Module: algorithms.optimize¶

nipy.algorithms.optimize.fmin_steepest(f, x0, fprime=None, xtol=0.0001, ftol=0.0001, maxiter=None, epsilon=1.4901161193847656e-08, callback=None, disp=True)

Minimize a function using a steepest gradient descent algorithm. This complements the collection of minimization routines provided in scipy.optimize. Steepest gradient iterations are cheaper than in the conjugate gradient or Newton methods, hence convergence may sometimes turn out faster algthough more iterations are typically needed.

Parameters: f : callable Function to be minimized x0 : array Starting point fprime : callable Function that computes the gradient of f xtol : float Relative tolerance on step sizes in line searches ftol : float Relative tolerance on function variations maxiter : int Maximum number of iterations epsilon : float or ndarray If fprime is approximated, use this value for the step size (can be scalar or vector). callback : callable Optional function called after each iteration is complete disp : bool Print convergence message if True x : array Gradient descent fix point, local minimizer of f