def thetaEuler(nx, ntmax, theta):
'''theta == 0 means forward Euler method, theta == 1 means backward Euler method. 0 <= theta <= 1. '''
dx = 1.0/nx
dt = 0.5*dx**2
x = []
for i in range(nx + 1):
x.append(i*dx)
u = np.zeros(nx + 1)
dimension = nx - 1
dimension, rowA, colA, dataA = spmv.sparseMatrix(dimension, theta*dt/dx**2)
dimension, rowB, colB, dataB = spmv.sparseMatrix(dimension, -(1-theta)*dt/dx**2)
f = np.zeros(len(x))
for i in range(len(x)):
f[i] = F(x[i])
tol = 0.0001
iterMax = 100
step = 0
while(True):
V = []
for element in u[1:-1]:
V.append(element)
u[1:-1] = spmvcg.conjugateGradient(dimension, rowA, colA, dataA, spmv.product(dimension, rowB, colB, dataB, u[1:-1]) + dt*f[1:-1], tol, iterMax)
step = step + 1
residual = 0
for i in range(len(V)):
residual = residual + (V[i] - u[1:-1][i])**2
#print step
#print residual
if (step > ntmax):
break
if (np.sqrt(residual) < 0.0000001):
break
return x, u
def main():
import sys
if (len(sys.argv) != 3):
print "Matrix dimension = argv[1], l = argv[2]. "
return -1
pr = cProfile.Profile()
pr.enable()
dimension = int(sys.argv[1])
l = float(sys.argv[2])
dimension, row, col, data = spmv.sparseMatrix(dimension, l)
b = np.zeros(dimension)
for i in range(dimension):
b[i] = i + 1
tol = 0.0001
iterMax = 100
x = conjugateGradient(dimension, row, col, data, b, tol, iterMax)
if (False):
print "Vector b: "
print b
print "Solution of Ax = b: "
print x
print "Ax = "
print spmv.product(dimension, row, col, data, x)
pr.disable()
pr.dump_stats("profile")
pr.print_stats()
return 0
