def try_factorization_and_reallocation(kkt,
linear_solver,
reallocation_factor,
max_iter,
timer=None):
if timer is None:
timer = HierarchicalTimer()
assert max_iter >= 1
for count in range(max_iter):
timer.start('symbolic')
"""
Performance could be improved significantly by only performing symbolic factorization once.
However, we first have to make sure the nonzero structure (and ordering of row and column arrays)
of the KKT matrix never changes. We have not had time to test this thoroughly, yet.
"""
res = linear_solver.do_symbolic_factorization(matrix=kkt,
raise_on_error=False)
timer.stop('symbolic')
if res.status == LinearSolverStatus.successful:
timer.start('numeric')
res = linear_solver.do_numeric_factorization(matrix=kkt,
raise_on_error=False)
timer.stop('numeric')
status = res.status
if status == LinearSolverStatus.not_enough_memory:
linear_solver.increase_memory_allocation(reallocation_factor)
else:
break
return status, count
def do_back_solve(self, rhs, timer=None):
"""
Performs a back solve with the factorized matrix. Should only be called after
do_numeric_factorixation.
Parameters
----------
rhs: MPIBlockVector
timer: pyutilib.misc.timing.HierarchicalTimer
Returns
-------
result: MPIBlockVector
"""
if timer is None:
timer = HierarchicalTimer()
timer.start('back_solve')
schur_complement_rhs = np.zeros(rhs.get_block(self.block_dim - 1).size,
dtype='d')
for ndx in self.local_block_indices:
A = self.block_matrix.get_block(self.block_dim - 1, ndx)
contribution = self.subproblem_solvers[ndx].do_back_solve(
rhs.get_block(ndx))
schur_complement_rhs -= A.tocsr().dot(contribution.flatten())
res = np.zeros(rhs.get_block(self.block_dim - 1).shape[0], dtype='d')
comm.Allreduce(schur_complement_rhs, res)
schur_complement_rhs = rhs.get_block(self.block_dim - 1) + res
result = rhs.copy_structure()
coupling = self.schur_complement_solver.do_back_solve(
schur_complement_rhs)
for ndx in self.local_block_indices:
A = self.block_matrix.get_block(self.block_dim - 1, ndx)
result.set_block(
ndx, self.subproblem_solvers[ndx].do_back_solve(
rhs.get_block(ndx) -
A.tocsr().transpose().dot(coupling.flatten())))
result.set_block(self.block_dim - 1, coupling)
timer.stop('back_solve')
return result
def check_convergence(self, barrier, timer=None):
"""
Parameters
----------
barrier: float
timer: HierarchicalTimer
Returns
-------
primal_inf: float
dual_inf: float
complimentarity_inf: float
"""
if timer is None:
timer = HierarchicalTimer()
interface = self.interface
slacks = interface.get_slacks()
timer.start('grad obj')
grad_obj = interface.get_obj_factor(
) * interface.evaluate_grad_objective()
timer.stop('grad obj')
timer.start('jac eq')
jac_eq = interface.evaluate_jacobian_eq()
timer.stop('jac eq')
timer.start('jac ineq')
jac_ineq = interface.evaluate_jacobian_ineq()
timer.stop('jac ineq')
timer.start('eq cons')
eq_resid = interface.evaluate_eq_constraints()
timer.stop('eq cons')
timer.start('ineq cons')
ineq_resid = interface.evaluate_ineq_constraints() - slacks
timer.stop('ineq cons')
primals = interface.get_primals()
duals_eq = interface.get_duals_eq()
duals_ineq = interface.get_duals_ineq()
duals_primals_lb = interface.get_duals_primals_lb()
duals_primals_ub = interface.get_duals_primals_ub()
duals_slacks_lb = interface.get_duals_slacks_lb()
duals_slacks_ub = interface.get_duals_slacks_ub()
primals_lb = interface.primals_lb()
primals_ub = interface.primals_ub()
primals_lb_mod = primals_lb.copy()
primals_ub_mod = primals_ub.copy()
primals_lb_mod[np.isneginf(
primals_lb)] = 0 # these entries get multiplied by 0
primals_ub_mod[np.isinf(
primals_ub)] = 0 # these entries get multiplied by 0
ineq_lb = interface.ineq_lb()
ineq_ub = interface.ineq_ub()
ineq_lb_mod = ineq_lb.copy()
ineq_ub_mod = ineq_ub.copy()
ineq_lb_mod[np.isneginf(
ineq_lb)] = 0 # these entries get multiplied by 0
ineq_ub_mod[np.isinf(ineq_ub)] = 0 # these entries get multiplied by 0
timer.start('grad_lag_primals')
grad_lag_primals = grad_obj + jac_eq.transpose() * duals_eq
grad_lag_primals += jac_ineq.transpose() * duals_ineq
grad_lag_primals -= duals_primals_lb
grad_lag_primals += duals_primals_ub
timer.stop('grad_lag_primals')
timer.start('grad_lag_slacks')
grad_lag_slacks = (-duals_ineq - duals_slacks_lb + duals_slacks_ub)
timer.stop('grad_lag_slacks')
timer.start('bound resids')
primals_lb_resid = (primals -
primals_lb_mod) * duals_primals_lb - barrier
primals_ub_resid = (primals_ub_mod -
primals) * duals_primals_ub - barrier
primals_lb_resid[np.isneginf(primals_lb)] = 0
primals_ub_resid[np.isinf(primals_ub)] = 0
slacks_lb_resid = (slacks - ineq_lb_mod) * duals_slacks_lb - barrier
slacks_ub_resid = (ineq_ub_mod - slacks) * duals_slacks_ub - barrier
slacks_lb_resid[np.isneginf(ineq_lb)] = 0
slacks_ub_resid[np.isinf(ineq_ub)] = 0
timer.stop('bound resids')
if eq_resid.size == 0:
max_eq_resid = 0
else:
max_eq_resid = np.max(np.abs(eq_resid))
if ineq_resid.size == 0:
max_ineq_resid = 0
else:
max_ineq_resid = np.max(np.abs(ineq_resid))
primal_inf = max(max_eq_resid, max_ineq_resid)
max_grad_lag_primals = np.max(np.abs(grad_lag_primals))
if grad_lag_slacks.size == 0:
max_grad_lag_slacks = 0
else:
max_grad_lag_slacks = np.max(np.abs(grad_lag_slacks))
dual_inf = max(max_grad_lag_primals, max_grad_lag_slacks)
if primals_lb_resid.size == 0:
max_primals_lb_resid = 0
else:
max_primals_lb_resid = np.max(np.abs(primals_lb_resid))
if primals_ub_resid.size == 0:
max_primals_ub_resid = 0
else:
max_primals_ub_resid = np.max(np.abs(primals_ub_resid))
if slacks_lb_resid.size == 0:
max_slacks_lb_resid = 0
else:
max_slacks_lb_resid = np.max(np.abs(slacks_lb_resid))
if slacks_ub_resid.size == 0:
max_slacks_ub_resid = 0
else:
max_slacks_ub_resid = np.max(np.abs(slacks_ub_resid))
complimentarity_inf = max(max_primals_lb_resid, max_primals_ub_resid,
max_slacks_lb_resid, max_slacks_ub_resid)
return primal_inf, dual_inf, complimentarity_inf
def solve(self, interface, timer=None, report_timing=False):
"""
Parameters
----------
interface: pyomo.contrib.interior_point.interface.BaseInteriorPointInterface
The interior point interface. This object handles the function evaluation,
building the KKT matrix, and building the KKT right hand side.
timer: HierarchicalTimer
report_timing: bool
"""
linear_solver = self.linear_solver
max_iter = self.max_iter
tol = self.tol
if timer is None:
timer = HierarchicalTimer()
timer.start('IP solve')
timer.start('init')
self._barrier_parameter = 0.1
self.set_interface(interface)
t0 = time.time()
primals = interface.init_primals().copy()
slacks = interface.init_slacks().copy()
duals_eq = interface.init_duals_eq().copy()
duals_ineq = interface.init_duals_ineq().copy()
duals_primals_lb = interface.init_duals_primals_lb().copy()
duals_primals_ub = interface.init_duals_primals_ub().copy()
duals_slacks_lb = interface.init_duals_slacks_lb().copy()
duals_slacks_ub = interface.init_duals_slacks_ub().copy()
self.process_init(primals, interface.primals_lb(),
interface.primals_ub())
self.process_init(slacks, interface.ineq_lb(), interface.ineq_ub())
self.process_init_duals_lb(duals_primals_lb,
self.interface.primals_lb())
self.process_init_duals_ub(duals_primals_ub,
self.interface.primals_ub())
self.process_init_duals_lb(duals_slacks_lb, self.interface.ineq_lb())
self.process_init_duals_ub(duals_slacks_ub, self.interface.ineq_ub())
interface.set_barrier_parameter(self._barrier_parameter)
alpha_primal_max = 1
alpha_dual_max = 1
self.logger.info('{_iter:<6}'
'{objective:<11}'
'{primal_inf:<11}'
'{dual_inf:<11}'
'{compl_inf:<11}'
'{barrier:<11}'
'{alpha_p:<11}'
'{alpha_d:<11}'
'{reg:<11}'
'{time:<7}'.format(_iter='Iter',
objective='Objective',
primal_inf='Prim Inf',
dual_inf='Dual Inf',
compl_inf='Comp Inf',
barrier='Barrier',
alpha_p='Prim Step',
alpha_d='Dual Step',
reg='Reg',
time='Time'))
reg_coef = 0
timer.stop('init')
status = InteriorPointStatus.error
for _iter in range(max_iter):
self._iter = _iter
interface.set_primals(primals)
interface.set_slacks(slacks)
interface.set_duals_eq(duals_eq)
interface.set_duals_ineq(duals_ineq)
interface.set_duals_primals_lb(duals_primals_lb)
interface.set_duals_primals_ub(duals_primals_ub)
interface.set_duals_slacks_lb(duals_slacks_lb)
interface.set_duals_slacks_ub(duals_slacks_ub)
timer.start('convergence check')
primal_inf, dual_inf, complimentarity_inf = \
self.check_convergence(barrier=0, timer=timer)
timer.stop('convergence check')
objective = interface.evaluate_objective()
self.logger.info('{_iter:<6}'
'{objective:<11.2e}'
'{primal_inf:<11.2e}'
'{dual_inf:<11.2e}'
'{compl_inf:<11.2e}'
'{barrier:<11.2e}'
'{alpha_p:<11.2e}'
'{alpha_d:<11.2e}'
'{reg:<11.2e}'
'{time:<7.3f}'.format(
_iter=_iter,
objective=objective,
primal_inf=primal_inf,
dual_inf=dual_inf,
compl_inf=complimentarity_inf,
barrier=self._barrier_parameter,
alpha_p=alpha_primal_max,
alpha_d=alpha_dual_max,
reg=reg_coef,
time=time.time() - t0))
if max(primal_inf, dual_inf, complimentarity_inf) <= tol:
status = InteriorPointStatus.optimal
break
timer.start('convergence check')
primal_inf, dual_inf, complimentarity_inf = \
self.check_convergence(barrier=self._barrier_parameter, timer=timer)
timer.stop('convergence check')
if max(primal_inf, dual_inf, complimentarity_inf) \
<= 0.1 * self._barrier_parameter:
# This comparison is made with barrier problem infeasibility.
# Sometimes have trouble getting dual infeasibility low enough
self.update_barrier_parameter()
interface.set_barrier_parameter(self._barrier_parameter)
timer.start('eval')
timer.start('eval kkt')
kkt = interface.evaluate_primal_dual_kkt_matrix(timer=timer)
timer.stop('eval kkt')
timer.start('eval rhs')
rhs = interface.evaluate_primal_dual_kkt_rhs(timer=timer)
timer.stop('eval rhs')
timer.stop('eval')
# Factorize linear system
timer.start('factorize')
reg_coef = self.factorize(kkt=kkt, timer=timer)
timer.stop('factorize')
timer.start('back solve')
with self.linear_solve_context:
self.logger.info('Iter: %s' % self._iter)
delta = linear_solver.do_back_solve(rhs)
timer.stop('back solve')
interface.set_primal_dual_kkt_solution(delta)
timer.start('frac boundary')
alpha_primal_max, alpha_dual_max = \
self.fraction_to_the_boundary()
timer.stop('frac boundary')
delta_primals = interface.get_delta_primals()
delta_slacks = interface.get_delta_slacks()
delta_duals_eq = interface.get_delta_duals_eq()
delta_duals_ineq = interface.get_delta_duals_ineq()
delta_duals_primals_lb = interface.get_delta_duals_primals_lb()
delta_duals_primals_ub = interface.get_delta_duals_primals_ub()
delta_duals_slacks_lb = interface.get_delta_duals_slacks_lb()
delta_duals_slacks_ub = interface.get_delta_duals_slacks_ub()
primals += alpha_primal_max * delta_primals
slacks += alpha_primal_max * delta_slacks
duals_eq += alpha_dual_max * delta_duals_eq
duals_ineq += alpha_dual_max * delta_duals_ineq
duals_primals_lb += alpha_dual_max * delta_duals_primals_lb
duals_primals_ub += alpha_dual_max * delta_duals_primals_ub
duals_slacks_lb += alpha_dual_max * delta_duals_slacks_lb
duals_slacks_ub += alpha_dual_max * delta_duals_slacks_ub
timer.stop('IP solve')
if report_timing:
print(timer)
return status
def evaluate_primal_dual_kkt_rhs(self, timer=None):
if timer is None:
timer = HierarchicalTimer()
timer.start('eval grad obj')
grad_obj = self.get_obj_factor() * self.evaluate_grad_objective()
timer.stop('eval grad obj')
timer.start('eval jac')
jac_eq = self._nlp.evaluate_jacobian_eq()
jac_ineq = self._nlp.evaluate_jacobian_ineq()
timer.stop('eval jac')
timer.start('eval cons')
eq_resid = self._nlp.evaluate_eq_constraints()
ineq_resid = self._nlp.evaluate_ineq_constraints() - self._slacks
timer.stop('eval cons')
timer.start('grad_lag_primals')
grad_lag_primals = (grad_obj +
jac_eq.transpose() * self._nlp.get_duals_eq() +
jac_ineq.transpose() * self._nlp.get_duals_ineq() -
self._barrier / (self._nlp.get_primals() - self._nlp.primals_lb()) +
self._barrier / (self._nlp.primals_ub() - self._nlp.get_primals()))
timer.stop('grad_lag_primals')
timer.start('grad_lag_slacks')
grad_lag_slacks = (-self._nlp.get_duals_ineq() -
self._barrier / (self._slacks - self._nlp.ineq_lb()) +
self._barrier / (self._nlp.ineq_ub() - self._slacks))
timer.stop('grad_lag_slacks')
rhs = BlockVector(4)
rhs.set_block(0, grad_lag_primals)
rhs.set_block(1, grad_lag_slacks)
rhs.set_block(2, eq_resid)
rhs.set_block(3, ineq_resid)
rhs = -rhs
return rhs
def evaluate_primal_dual_kkt_matrix(self, timer=None):
if timer is None:
timer = HierarchicalTimer()
timer.start('eval hess')
hess_block = self._nlp.evaluate_hessian_lag()
timer.stop('eval hess')
timer.start('eval jac')
jac_eq = self._nlp.evaluate_jacobian_eq()
jac_ineq = self._nlp.evaluate_jacobian_ineq()
timer.stop('eval jac')
duals_primals_lb = self._duals_primals_lb
duals_primals_ub = self._duals_primals_ub
duals_slacks_lb = self._duals_slacks_lb
duals_slacks_ub = self._duals_slacks_ub
primals = self._nlp.get_primals()
timer.start('hess block')
data = (duals_primals_lb/(primals - self._nlp.primals_lb()) +
duals_primals_ub/(self._nlp.primals_ub() - primals))
n = self._nlp.n_primals()
indices = np.arange(n)
hess_block.row = np.concatenate([hess_block.row, indices])
hess_block.col = np.concatenate([hess_block.col, indices])
hess_block.data = np.concatenate([hess_block.data, data])
timer.stop('hess block')
timer.start('slack block')
data = (duals_slacks_lb/(self._slacks - self._nlp.ineq_lb()) +
duals_slacks_ub/(self._nlp.ineq_ub() - self._slacks))
n = self._nlp.n_ineq_constraints()
indices = np.arange(n)
slack_block = scipy.sparse.coo_matrix((data, (indices, indices)), shape=(n, n))
timer.stop('slack block')
timer.start('regularization block')
eq_reg_blk = scipy.sparse.identity(self._nlp.n_eq_constraints(), format='coo')
eq_reg_blk.data.fill(0)
timer.stop('regularization block')
timer.start('set block')
kkt = BlockMatrix(4, 4)
kkt.set_block(0, 0, hess_block)
kkt.set_block(1, 1, slack_block)
kkt.set_block(2, 0, jac_eq)
kkt.set_block(0, 2, jac_eq.transpose())
kkt.set_block(3, 0, jac_ineq)
kkt.set_block(0, 3, jac_ineq.transpose())
kkt.set_block(3, 1, -scipy.sparse.identity(
self._nlp.n_ineq_constraints(),
format='coo'))
kkt.set_block(1, 3, -scipy.sparse.identity(
self._nlp.n_ineq_constraints(),
format='coo'))
kkt.set_block(2, 2, eq_reg_blk)
timer.stop('set block')
return kkt
def test_hierarchical_timer(self):
timer = HierarchicalTimer()
timer.start('all')
for i in range(10):
timer.start('a')
for i in range(5):
timer.start('aa')
timer.stop('aa')
timer.start('ab')
timer.stop('ab')
timer.stop('a')
timer.start('b')
timer.stop('b')
timer.start('a')
with self.assertRaisesRegex(
ValueError,
'all is not the currently active timer. The only timer that can currently be stopped is all.a'
):
timer.stop('all')
timer.stop('a')
timer.stop('all')
a_percent = timer.get_relative_percent_time('all.a')
aa_percent = timer.get_relative_percent_time('all.a.aa')
aa_total_percent = timer.get_total_percent_time('all.a.aa')
self.assertAlmostEqual(aa_total_percent,
a_percent / 100 * aa_percent / 100 * 100)
self.assertAlmostEqual(timer.get_num_calls('all.a'), 11)
self.assertAlmostEqual(timer.get_num_calls('all.a.ab'), 10)
self.assertAlmostEqual(timer.get_num_calls('all.a.aa'), 50)
timer.get_total_time('all.b')
print(timer)
def do_numeric_factorization(
self,
matrix: MPIBlockMatrix,
raise_on_error: bool = True,
timer: Optional[HierarchicalTimer] = None) -> LinearSolverResults:
"""
Perform numeric factorization:
* perform numeric factorization on each diagonal block
* form and communicate the Schur-Complement
* factorize the schur-complement
This method should only be called after do_symbolic_factorization.
Parameters
----------
matrix: MPIBlockMatrix
A Pynumero MPIBlockMatrix. This is the A matrix in Ax=b
raise_on_error: bool
If False, an error will not be raised if an error occurs during symbolic factorization. Instead the
status attribute of the results object will indicate an error ocurred.
timer: pyutilib.misc.timing.HierarchicalTimer
A timer for profiling.
Returns
-------
res: LinearSolverResults
The results object
"""
if timer is None:
timer = HierarchicalTimer()
timer.start('numeric')
self.block_matrix = block_matrix = matrix
res = LinearSolverResults()
res.status = LinearSolverStatus.successful
timer.start('form SC')
for ndx in self.local_block_indices:
timer.start('factorize')
sub_res = self.subproblem_solvers[ndx].do_numeric_factorization(
matrix=block_matrix.get_block(ndx, ndx), raise_on_error=False)
timer.stop('factorize')
_process_sub_results(res, sub_res)
if res.status not in {
LinearSolverStatus.successful, LinearSolverStatus.warning
}:
break
res = _gather_results(res)
if res.status not in {
LinearSolverStatus.successful, LinearSolverStatus.warning
}:
if raise_on_error:
raise RuntimeError(
'Numeric factorization unsuccessful; status: ' +
str(res.status))
else:
return res
# in a scipy csr_matrix,
# data contains the values
# indices contains the column indices
# indptr contains the number of nonzeros in the row
self.schur_complement.data = np.zeros(self.schur_complement.data.size,
dtype=np.double)
for ndx in self.local_block_indices:
A = block_matrix.get_block(self.block_dim - 1, ndx).tocsr()
_rhs = np.zeros(A.shape[1], dtype=np.double)
solver = self.subproblem_solvers[ndx]
for row_ndx in range(A.shape[0]):
row_nnz = A.indptr[row_ndx + 1] - A.indptr[row_ndx]
if row_nnz != 0:
for indptr in range(A.indptr[row_ndx],
A.indptr[row_ndx + 1]):
col = A.indices[indptr]
val = A.data[indptr]
_rhs[col] += val
timer.start('back solve')
contribution = solver.do_back_solve(_rhs)
timer.stop('back solve')
timer.start('dot product')
contribution = A.dot(contribution)
timer.stop('dot product')
nonzero_contribution_indices = contribution.nonzero()[0]
sc_col = row_ndx
for sc_row in nonzero_contribution_indices:
val_ndx = self.sc_coordinate_to_value_ndx_map[(sc_row,
sc_col)]
self.schur_complement.data[val_ndx] -= contribution[
sc_row]
for indptr in range(A.indptr[row_ndx],
A.indptr[row_ndx + 1]):
col = A.indices[indptr]
val = A.data[indptr]
_rhs[col] -= val
timer.start('communicate')
sc = np.zeros(self.schur_complement.data.size, dtype=np.double)
comm.Allreduce(self.schur_complement.data, sc)
self.schur_complement.data = sc
sc = self.schur_complement + block_matrix.get_block(
self.block_dim - 1, self.block_dim - 1)
timer.stop('communicate')
timer.stop('form SC')
timer.start('factor SC')
sub_res = self.schur_complement_solver.do_symbolic_factorization(
sc, raise_on_error=raise_on_error)
_process_sub_results(res, sub_res)
if res.status not in {
LinearSolverStatus.successful, LinearSolverStatus.warning
}:
return res
sub_res = self.schur_complement_solver.do_numeric_factorization(sc)
_process_sub_results(res, sub_res)
timer.stop('factor SC')
timer.stop('numeric')
return res
def do_symbolic_factorization(
self,
matrix: MPIBlockMatrix,
raise_on_error: bool = True,
timer: Optional[HierarchicalTimer] = None) -> LinearSolverResults:
"""
Perform symbolic factorization. This performs symbolic factorization for each diagonal block and
collects some information on the structure of the schur complement for sparse communication in
the numeric factorization phase.
Parameters
----------
matrix: MPIBlockMatrix
A Pynumero MPIBlockMatrix. This is the A matrix in Ax=b
raise_on_error: bool
If False, an error will not be raised if an error occurs during symbolic factorization. Instead the
status attribute of the results object will indicate an error ocurred.
timer: pyutilib.misc.timing.HierarchicalTimer
A timer for profiling.
Returns
-------
res: LinearSolverResults
The results object
"""
if timer is None:
timer = HierarchicalTimer()
timer.start('symbolic')
block_matrix = matrix
nbrows, nbcols = block_matrix.bshape
if nbrows != nbcols:
raise ValueError('The block matrix provided is not square.')
self.block_dim = nbrows
nrows, ncols = block_matrix.shape
if nrows != ncols:
raise ValueError('The block matrix provided is not square.')
self.dim = nrows
# split up the blocks between ranks
self.local_block_indices = list()
self.block_indices_by_rank = {_rank: list() for _rank in range(size)}
for ndx in range(self.block_dim - 1):
self.block_indices_by_rank[block_matrix.rank_ownership[
ndx, ndx]].append(ndx)
if ((block_matrix.rank_ownership[ndx, ndx] == rank) or
(block_matrix.rank_ownership[ndx, ndx] == -1 and rank == 0)):
self.local_block_indices.append(ndx)
res = LinearSolverResults()
res.status = LinearSolverStatus.successful
timer.start('factorize')
for ndx in self.local_block_indices:
sub_res = self.subproblem_solvers[ndx].do_symbolic_factorization(
matrix=block_matrix.get_block(ndx, ndx), raise_on_error=False)
_process_sub_results(res, sub_res)
if res.status not in {
LinearSolverStatus.successful, LinearSolverStatus.warning
}:
break
timer.stop('factorize')
res = _gather_results(res)
if res.status not in {
LinearSolverStatus.successful, LinearSolverStatus.warning
}:
if raise_on_error:
raise RuntimeError(
'Symbolic factorization unsuccessful; status: ' +
str(res.status))
else:
return res
timer.start('sc_structure')
self._get_sc_structure(block_matrix=block_matrix)
timer.stop('sc_structure')
timer.stop('symbolic')
return res
