# Source code for bayespy.utils.optimize

```################################################################################
# Copyright (C) 2011-2013 Jaakko Luttinen
#
################################################################################

import numpy as np
from scipy import optimize

[docs]def minimize(f, x0, maxiter=None, verbose=False):
"""
Simple wrapper for SciPy's optimize.

The given function must return a tuple: (value, gradient).
"""
options = {'disp': verbose}
if maxiter is not None:
options['maxiter'] = maxiter
opt = optimize.minimize(f, x0, jac=True, method='CG', options=options)
return opt.x

[docs]def check_gradient(f, x0, verbose=True, epsilon=optimize.optimize._epsilon, return_abserr=False):
"""
Simple wrapper for SciPy's gradient checker.

The given function must return a tuple: (value, gradient).

Returns absolute and relative errors
"""
df = f(x0)[1]
df_num = optimize.approx_fprime(x0,
lambda x: f(x)[0],
epsilon)
abserr = np.linalg.norm(df-df_num)
norm_num = np.linalg.norm(df_num)
if abserr == 0 and norm_num == 0:
err = 0
else:
err = abserr / norm_num
if verbose:
print("Norm of numerical gradient: %g" % np.linalg.norm(df_num))
print("Norm of function gradient:  %g" % np.linalg.norm(df))
print("Gradient relative error = %g and absolute error = %g" %
(err,
abserr))

return (abserr, err)

```