def reorder_matrix(n, rowind, colptr, icntl=[0, 6], jcntl=[0, 0], weight=[2, 1]):
"""Helper function called by `sloan` and `rcm`.
It performs the bulk of the work when applying Sloan's method or the
reverse Cuthill-McKee algorithm to a symmetric sparse matrix.
"""
# Make room for pattern of the whole matrix and adjust to be 1-based
icptr = colptr.copy() + 1
irn = np.empty(2 * (icptr[-1] - 1), dtype=np.int32)
irn[:icptr[-1] - 1] = rowind.copy() + 1
# Check data
info = _mc60.mc60ad(irn, icptr, icntl)
# Compute supervariables
nsup, svar, vars = _mc60.mc60bd(irn, icptr)
# Permute reduced matrix
permsv = np.empty(nsup, dtype=np.int32)
pair = np.empty((2, nsup / 2), dtype=np.int32)
info = _mc60.mc60cd(n, irn, icptr[:nsup + 1], vars[:nsup],
jcntl, permsv, weight, pair)
# Compute profile, maximum wavefront, semi-bandwidth and root-mean-square
# wavefront of the permuted matrix
rinfo = _mc60.mc60fd(n, irn, icptr[:nsup + 1], vars[:nsup], permsv)
# Obtain variable permutation from supervariable permutation
perm, possv = _mc60.mc60dd(svar, vars[:nsup], permsv)
# Adjust permutation to make it 0-based
perm -= 1
# There are other things to return!
return (perm, rinfo)
def test_spec_sheet(self):
"""Solve example from the spec sheet.
Ordering
http://www.hsl.rl.ac.uk/specs/mc60.pdf
"""
icntl = np.array([0, 6], dtype=np.int32) # Abort on error
jcntl = np.array([0, 0], dtype=np.int32) # Sloan's alg with auto choice
weight = np.array([2, 1]) # Weights in Sloan's alg
# Store lower triangle of symmetric matrix in csr format (1-based)
n = 5
icptr = np.array([1, 6, 8, 9, 10, 11], dtype=np.int32) # nnz = 10
irn = np.empty(2 * (icptr[-1] - 1), dtype=np.int32)
irn[:icptr[-1] - 1] = np.array([1, 2, 3,
4, 5, 2, 3, 3, 4, 5], dtype=np.int32)
# Check data
info = _mc60.mc60ad(irn, icptr, icntl)
# Compute supervariables
nsup, svar, vars = _mc60.mc60bd(irn, icptr)
assert nsup == 4
print 'The number of supervariables is ', nsup
# Permute reduced matrix
permsv = np.empty(nsup, dtype=np.int32)
pair = np.empty((2, nsup / 2), dtype=np.int32)
info = _mc60.mc60cd(n, irn, icptr[:nsup + 1],
vars[:nsup], jcntl, permsv, weight, pair)
# Compute profile and wavefront
rinfo = _mc60.mc60fd(n, irn, icptr[:nsup + 1], vars[:nsup], permsv)
# Obtain variable permutation from supervariable permutation
perm, possv = _mc60.mc60dd(svar, vars[:nsup], permsv)
assert np.array_equal(perm, np.array([3, 5, 4, 1, 2], np.int32))
assert rinfo[0] == 10
weight = np.array([2, 1]) # Weights in Sloan's alg
# Store lower triangle of symmetric matrix in csr format
# We make copies of M.ip and M.ind because MC60 will modify them
n = M.nrow
icptr = M.ip.copy() + 1 # Convert to 1-based indexing (for Fortran)
# Make room for pattern of the whole matrix
irn = np.empty(2 * (icptr[-1] - 1), dtype=np.int32)
irn[:icptr[-1] - 1] = M.ind.copy() + 1
# Check data
info = _mc60.mc60ad(irn, icptr, icntl)
# Compute supervariables
nsup, svar, vars = _mc60.mc60bd(irn, icptr)
print 'The number of supervariables is ', nsup
# Permute reduced matrix
permsv = np.empty(nsup, dtype=np.int32)
pair = np.empty((2, nsup / 2), dtype=np.int32)
info = _mc60.mc60cd(n, irn, icptr[:nsup + 1], vars[:nsup],
jcntl, permsv, weight, pair)
# Compute profile and wavefront
rinfo = _mc60.mc60fd(n, irn, icptr[:nsup + 1], vars[:nsup], permsv)
# Obtain variable permutation from supervariable permutation
perm, possv = _mc60.mc60dd(svar, vars[:nsup], permsv)
np.set_printoptions(precision=3, linewidth=80, threshold=10, edgeitems=3)