In [55]:
import numpy as np
import scipy.sparse as sparse
import scipy.linalg as sla
import scipy.sparse.linalg as spla
import matplotlib.pyplot as plt
%matplotlib inline


Let's make a random sparse matrix

First we'll set the density so that $$density = \frac{nnz(A)}{n^2}$$

In [75]:
n = 100
density = 10.0 / n # 5 points per row
nnz = int(n*n*density)


Now make the entries:

In [76]:
row = np.random.random_integers(low=0, high=n-1, size=nnz)
col = np.random.random_integers(low=0, high=n-1, size=nnz)
data = np.ones(nnz, dtype=float)

A = sparse.coo_matrix((data, (row, col)), shape=(n, n))
print(A.dtype)

float64


But let's make it positive definite:

In [77]:
A.data[:] = -1.0                   # -1 for off-diagonals
rowsum = -np.array(A.sum(axis=1)) + 1 # positive rowsum
rowsum = rowsum.ravel()
A.setdiag(rowsum)

In [78]:
u = np.random.rand(n)
v = np.random.rand(n)

In [79]:
A = A.tocsc()
%timeit s = spla.splu(A)

1000 loops, best of 3: 399 µs per loop

In [80]:
plt.spy(A, marker='.')

Out[80]:
<matplotlib.lines.Line2D at 0x10d8002b0>
In [81]:
B = A.todense()

In [82]:
%timeit p, L, U = sla.lu(B)

1000 loops, best of 3: 216 µs per loop

In [83]:
plt.spy(s.L, marker='.')

Out[83]:
<matplotlib.lines.Line2D at 0x10d89a0f0>
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