def steepest_descent(A, b, x_init=None, M=10000, ε=10e-10, check_spd=True):
"""
finds a solution to the linear system Ax = b via steepest descent
inputs:
A is an nxn SPD matrix
b is an n x 1 vector
x0 is the initial estimate vector (nx1) (default is zero vector)
M is max number of iterations (default 10000)
ε is machine precision (default .0001)
if check_spd, the program will fail in the event that the system is not
spd
note:
- uses size of || x_{k+1} - x_k ||_2^2 as stopping condition
- please don't break this code with weird stuff (more testing needed)
"""
if not spd(A):
raise Exception("""
A must be positive definite!
pass check_spd=False to bypass""")
# initialize x_0 as zero vector if one wasn't provided
if x_init is None:
x = np.zeros_like(b, np.float)
else:
x = x_init
for k in range(M):
v = b - A.dot(x)
u = A.dot(v)
t = v.T.dot(v) / u.T.dot(v)
x, x_old = ( x + t*v , x)
x_change = norm(x-x_old)
if x_change < ε:
break
#print(locals())
#input('...')
else:
print('warning: exhausted max iterations (M={})'.format(M))
summary = "terminated in {} iterations (out of M={}) with tolerance ε={}".format(k+1, M, ε)
print(summary)
return x
def conjugate_gradient(A, b, M=10000, x=None, tol=10e-10, check_spd=True):
"""
conjugate gradient solution of Ax = b
INPUTS
A: a nxn array representing a symmetric positive definite matrix
b: a nx1 array
M: max iterations to run (see below)
x: a starting guess (default is zero vector)
tol: tolerance (used for stopping condition)
check_spd: pre-check that A is in fact symmetric positive definite
OUTPUT
x: the approximate solution to the system Ax=b
NOTES:
- M should be superfluous; theoretically this method with terminate
successfuly in n == b.size iterations, provided A is s.p.d. No such
guarantee is made if A is not s.p.d.
- Similarly, a starting guess of x=0 is typically desired, as it minimizes
computational error.
- If A is not symmetric positive definite and check_spd==True, then a
generic Exception will be thrown, terminating execution.
"""
assert A.shape[1] == b.shape[0], "system doesn't match"
if not spd(A):
raise Exception("""
A is not positive definite; aborting.
pass check_false=False to bypass""")
# set an initial guess or check inputted
if x is None:
x = np.zeros_like(b) # creates an nx1 zero vector
else:
assert x.shape == b.shape, "system doesn't match"
r = b - A.dot(x)
c = vdot(r,r)
v = r
for i in range(M):
if np.sqrt(c) < tol:
print("tolerance reached!")
break
u = A.dot(v)
alpha = c / (u.T.dot(v))
x = x + alpha*v
r = r - alpha*u
d = vdot(r,r)
if np.sqrt(d) < tol:
print("tolerance reached!")
break
else:
v = r + (d/c)*v
c = d
else:
print("max ({}) iterations exhausted".format(i+1))
print("finished in {} iterations".format(i))
return x
