def presolve(self, mtx):
"""Prepare A^{-1} B^T for the Schur complement."""
mtx_a = mtx['A']
mtx_bt = mtx['BT']
output('full A size: %.3f MB' % (8.0 * nm.prod(mtx_a.shape) / 1e6))
output('full B size: %.3f MB' % (8.0 * nm.prod(mtx_bt.shape) / 1e6))
ls = Solver.any_from_conf(self.problem.ls_conf,
presolve=True,
mtx=mtx_a)
if self.mode == 'explicit':
tt = time.clock()
mtx_aibt = nm.zeros(mtx_bt.shape, dtype=mtx_bt.dtype)
for ic in xrange(mtx_bt.shape[1]):
mtx_aibt[:, ic] = ls(mtx_bt[:, ic].toarray().squeeze())
output('mtx_aibt: %.2f s' % (time.clock() - tt))
action_aibt = MatrixAction.from_array(mtx_aibt)
else:
##
# c: 30.08.2007, r: 13.02.2008
def fun_aibt(vec):
# Fix me for sparse mtx_bt...
rhs = sc.dot(mtx_bt, vec)
out = ls(rhs)
return out
action_aibt = MatrixAction.from_function(
fun_aibt, (mtx_a.shape[0], mtx_bt.shape[1]), nm.float64)
mtx['action_aibt'] = action_aibt
def presolve(self, mtx):
"""Prepare A^{-1} B^T for the Schur complement."""
mtx_a = mtx['A']
mtx_bt = mtx['BT']
output('full A size: %.3f MB' % (8.0 * nm.prod(mtx_a.shape) / 1e6))
output('full B size: %.3f MB' % (8.0 * nm.prod(mtx_bt.shape) / 1e6))
ls = Solver.any_from_conf(self.problem.ls_conf,
presolve=True, mtx=mtx_a)
if self.mode == 'explicit':
tt = time.clock()
mtx_aibt = nm.zeros(mtx_bt.shape, dtype=mtx_bt.dtype)
for ic in xrange(mtx_bt.shape[1]):
mtx_aibt[:,ic] = ls(mtx_bt[:,ic].toarray().squeeze())
output('mtx_aibt: %.2f s' % (time.clock() - tt))
action_aibt = MatrixAction.from_array(mtx_aibt)
else:
##
# c: 30.08.2007, r: 13.02.2008
def fun_aibt(vec):
# Fix me for sparse mtx_bt...
rhs = sc.dot(mtx_bt, vec)
out = ls(rhs)
return out
action_aibt = MatrixAction.from_function(fun_aibt,
(mtx_a.shape[0],
mtx_bt.shape[1]),
nm.float64)
mtx['action_aibt'] = action_aibt