我在 Python 中有一个具有狄利克雷条件的 2D 拉普拉斯的 SOR 解决方案。如果 omega 设置为 1.0(使其成为 Jacobi 方法),则解收敛得很好。但是使用给定的欧米茄,无法达到目标误差,因为解决方案在某些时候变得疯狂,无法收敛。为什么它不与给定的欧米茄公式收敛? repl.it 上的实时示例
from math import sin, exp, pi, sqrt
m = 16
m1 = m + 1
m2 = m + 2
grid = [[0.0]*m2 for i in xrange(m2)]
newGrid = [[0.0]*m2 for i in xrange(m2)]
for x in xrange(m2):
grid[x][0] = sin(pi * x / m1)
grid[x][m1] = sin(pi * x / m1)*exp(-x/m1)
omega = 2 #initial value, iter = 0
ro = 1 - pi*pi / (4.0 * m * m) #spectral radius
print "ro", ro
print "omega limit", 2 / (ro*ro) - 2/ro*sqrt(1/ro/ro - 1)
def next_omega(prev_omega):
return 1.0 / (1 - ro * ro * prev_omega / 4.0)
for iteration in xrange(50):
print "iter", iteration,
omega = next_omega(omega)
print "omega", omega,
for x in range(1, m1):
for y in range(1, m1):
newGrid[x][y] = grid[x][y] + 0.25 * omega * \
(grid[x - 1][y] + \
grid[x + 1][y] + \
grid[x][y - 1] + \
grid[x][y + 1] - 4.0 * grid[x][y])
err = sum([abs(newGrid[x][y] - grid[x][y]) \
for x in range(1, m1) \
for y in range(1, m1)])
print err,
for x in range(1, m1):
for y in range(1, m1):
grid[x][y] = newGrid[x][y]
print