def jacobi_method(A, b, _iter=100):
"""
Iterative algorithm for approximating the solutions of a diagonally dominant system of linear equations.
Parameters
----------
A : ndarray or Expr - 2d
Input matrix
b : ndarray or Expr - vector
RHS vector
_iter : int
Times of iteration needed, default to be 100
Returns
-------
result : Expr - vector
Approximated solution.
"""
util.Assert.eq(A.shape[0], b.shape[0])
x = expr.zeros((A.shape[0],))
D = expr.diag(A)
R = A - expr.diagflat(D)
for i in xrange(_iter):
x = (b - expr.dot(R, x)) / D
return x
def jacobi_method(A, b, _iter=100):
"""
Iterative algorithm for approximating the solutions of a diagonally dominant system of linear equations.
Parameters
----------
A : ndarray or Expr - 2d
Input matrix
b : ndarray or Expr - vector
RHS vector
_iter : int
Times of iteration needed, default to be 100
Returns
-------
result : Expr - vector
Approximated solution.
"""
util.Assert.eq(A.shape[0], b.shape[0])
x = expr.zeros((A.shape[0], ))
D = expr.diag(A)
R = A - expr.diagflat(D)
for i in xrange(_iter):
x = (b - expr.dot(R, x)) / D
return x
def fn3(): a = expr.ones((10,)) g = expr.diag(a) g += expr.ones((10,10)) g = expr.diagonal(g) print g.optimized()
def fn3(): a = expr.ones((10,)) g = expr.diag(a) g += expr.ones((10, 10)) g = expr.diagonal(g) print g.optimized()