return [v_i + step_size * direction_i for v_i, direction_i in zip(v, direction)]
def sum_of_squares_gradient(v):
return [2 * v_i for v_i in v]
if isMain:
# pick a random starting point
v = [random.randint(-10, 10) for _ in range(3)]
tolerance = .00000001
while True:
gradient = sum_of_squares_gradient(v)
next_v = step(v, gradient, -0.01)
if Ch4.distance(next_v, v) < tolerance:
break
v = next_v
print("Approx solutions: ", v)
# But what should the step size be?
# This is a safe apply function, just in case we try a bad step size
def safe(f):
"""
Return a new function that is the same as f, except
that it outputs infinity whenever f produces an error
"""
def safe_f(*args, **kwargs):
try:
