def __init__(self):
self._config = IpoptConfig()
self._solver_options = dict()
self._writer = NLWriter()
self._filename = None
self._dual_sol = dict()
self._primal_sol = ComponentMap()
self._reduced_costs = ComponentMap()
class Ipopt(PersistentSolver):
def __init__(self):
self._config = IpoptConfig()
self._solver_options = dict()
self._writer = NLWriter()
self._filename = None
self._dual_sol = dict()
self._primal_sol = ComponentMap()
self._reduced_costs = ComponentMap()
self._last_results_object: Optional[Results] = None
def available(self):
if self.config.executable.path() is None:
return self.Availability.NotFound
return self.Availability.FullLicense
def version(self):
results = subprocess.run([str(self.config.executable), '--version'],
timeout=1,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
universal_newlines=True)
version = results.stdout.splitlines()[0]
version = version.split(' ')[1]
version = version.strip()
version = tuple(int(i) for i in version.split('.'))
return version
def nl_filename(self):
if self._filename is None:
return None
else:
return self._filename + '.nl'
def sol_filename(self):
if self._filename is None:
return None
else:
return self._filename + '.sol'
def options_filename(self):
if self._filename is None:
return None
else:
return self._filename + '.opt'
@property
def config(self):
return self._config
@config.setter
def config(self, val):
self._config = val
@property
def ipopt_options(self):
"""
Returns
-------
ipopt_options: dict
A dictionary mapping solver options to values for those options. These
are solver specific.
"""
return self._solver_options
@ipopt_options.setter
def ipopt_options(self, val: Dict):
self._solver_options = val
@property
def update_config(self):
return self._writer.update_config
@property
def writer(self):
return self._writer
@property
def symbol_map(self):
return self._writer.symbol_map
def set_instance(self, model):
self._writer.set_instance(model)
def add_variables(self, variables: List[_GeneralVarData]):
self._writer.add_variables(variables)
def add_params(self, params: List[_ParamData]):
self._writer.add_params(params)
def add_constraints(self, cons: List[_GeneralConstraintData]):
self._writer.add_constraints(cons)
def add_block(self, block: _BlockData):
self._writer.add_block(block)
def remove_variables(self, variables: List[_GeneralVarData]):
self._writer.remove_variables(variables)
def remove_params(self, params: List[_ParamData]):
self._writer.remove_params(params)
def remove_constraints(self, cons: List[_GeneralConstraintData]):
self._writer.remove_constraints(cons)
def remove_block(self, block: _BlockData):
self._writer.remove_block(block)
def set_objective(self, obj: _GeneralObjectiveData):
self._writer.set_objective(obj)
def update_variables(self, variables: List[_GeneralVarData]):
self._writer.update_variables(variables)
def update_params(self):
self._writer.update_params()
def _write_options_file(self):
f = open(self._filename + '.opt', 'w')
for k, val in self.ipopt_options.items():
if k not in ipopt_command_line_options:
f.write(str(k) + ' ' + str(val) + '\n')
f.close()
def solve(self, model, timer: HierarchicalTimer = None):
avail = self.available()
if not avail:
raise PyomoException(
f'Solver {self.__class__} is not available ({avail}).')
if self._last_results_object is not None:
self._last_results_object.solution_loader.invalidate()
if timer is None:
timer = HierarchicalTimer()
try:
TempfileManager.push()
if self.config.filename is None:
nl_filename = TempfileManager.create_tempfile(suffix='.nl')
self._filename = nl_filename.split('.')[0]
else:
self._filename = self.config.filename
TempfileManager.add_tempfile(self._filename + '.nl',
exists=False)
TempfileManager.add_tempfile(self._filename + '.sol', exists=False)
TempfileManager.add_tempfile(self._filename + '.opt', exists=False)
self._write_options_file()
timer.start('write nl file')
self._writer.write(model, self._filename + '.nl', timer=timer)
timer.stop('write nl file')
res = self._apply_solver(timer)
self._last_results_object = res
if self.config.report_timing:
logger.info('\n' + str(timer))
return res
finally:
# finally, clean any temporary files registered with the
# temp file manager, created/populated *directly* by this
# plugin.
TempfileManager.pop(remove=not self.config.keepfiles)
if not self.config.keepfiles:
self._filename = None
def _parse_sol(self):
solve_vars = self._writer.get_ordered_vars()
solve_cons = self._writer.get_ordered_cons()
results = Results()
f = open(self._filename + '.sol', 'r')
all_lines = list(f.readlines())
f.close()
termination_line = all_lines[1]
if 'Optimal Solution Found' in termination_line:
results.termination_condition = TerminationCondition.optimal
elif 'Problem may be infeasible' in termination_line:
results.termination_condition = TerminationCondition.infeasible
elif 'problem might be unbounded' in termination_line:
results.termination_condition = TerminationCondition.unbounded
elif 'Maximum Number of Iterations Exceeded' in termination_line:
results.termination_condition = TerminationCondition.maxIterations
elif 'Maximum CPU Time Exceeded' in termination_line:
results.termination_condition = TerminationCondition.maxTimeLimit
else:
results.termination_condition = TerminationCondition.unknown
n_cons = len(solve_cons)
n_vars = len(solve_vars)
dual_lines = all_lines[12:12 + n_cons]
primal_lines = all_lines[12 + n_cons:12 + n_cons + n_vars]
rc_upper_info_line = all_lines[12 + n_cons + n_vars + 1]
assert rc_upper_info_line.startswith('suffix')
n_rc_upper = int(rc_upper_info_line.split()[2])
assert 'ipopt_zU_out' in all_lines[12 + n_cons + n_vars + 2]
upper_rc_lines = all_lines[12 + n_cons + n_vars + 3:12 + n_cons +
n_vars + 3 + n_rc_upper]
rc_lower_info_line = all_lines[12 + n_cons + n_vars + 3 + n_rc_upper]
assert rc_lower_info_line.startswith('suffix')
n_rc_lower = int(rc_lower_info_line.split()[2])
assert 'ipopt_zL_out' in all_lines[12 + n_cons + n_vars + 3 +
n_rc_upper + 1]
lower_rc_lines = all_lines[12 + n_cons + n_vars + 3 + n_rc_upper +
2:12 + n_cons + n_vars + 3 + n_rc_upper +
2 + n_rc_lower]
self._dual_sol = dict()
self._primal_sol = ComponentMap()
self._reduced_costs = ComponentMap()
for ndx, dual in enumerate(dual_lines):
dual = float(dual)
con = solve_cons[ndx]
self._dual_sol[con] = dual
for ndx, primal in enumerate(primal_lines):
primal = float(primal)
var = solve_vars[ndx]
self._primal_sol[var] = primal
for rcu_line in upper_rc_lines:
split_line = rcu_line.split()
var_ndx = int(split_line[0])
rcu = float(split_line[1])
var = solve_vars[var_ndx]
self._reduced_costs[var] = rcu
for rcl_line in lower_rc_lines:
split_line = rcl_line.split()
var_ndx = int(split_line[0])
rcl = float(split_line[1])
var = solve_vars[var_ndx]
if var in self._reduced_costs:
if abs(rcl) > abs(self._reduced_costs[var]):
self._reduced_costs[var] = rcl
else:
self._reduced_costs[var] = rcl
for var in solve_vars:
if var not in self._reduced_costs:
self._reduced_costs[var] = 0
if results.termination_condition == TerminationCondition.optimal and self.config.load_solution:
for v, val in self._primal_sol.items():
v.set_value(val, skip_validation=True)
if self._writer.get_active_objective() is None:
results.best_feasible_objective = None
else:
results.best_feasible_objective = value(
self._writer.get_active_objective().expr)
elif results.termination_condition == TerminationCondition.optimal:
if self._writer.get_active_objective() is None:
results.best_feasible_objective = None
else:
obj_expr_evaluated = replace_expressions(
self._writer.get_active_objective().expr,
substitution_map={
id(v): val
for v, val in self._primal_sol.items()
},
descend_into_named_expressions=True,
remove_named_expressions=True)
results.best_feasible_objective = value(obj_expr_evaluated)
elif self.config.load_solution:
raise RuntimeError(
'A feasible solution was not found, so no solution can be loaded.'
'Please set opt.config.load_solution=False and check '
'results.termination_condition and '
'resutls.best_feasible_objective before loading a solution.')
results.solution_loader = PersistentSolutionLoader(solver=self)
return results
def _apply_solver(self, timer: HierarchicalTimer):
config = self.config
if config.time_limit is not None:
timeout = config.time_limit + min(max(1, 0.01 * config.time_limit),
100)
else:
timeout = None
ostreams = [
LogStream(level=self.config.log_level,
logger=self.config.solver_output_logger)
]
if self.config.stream_solver:
ostreams.append(sys.stdout)
cmd = [
str(config.executable), self._filename + '.nl', '-AMPL',
'option_file_name=' + self._filename + '.opt'
]
if 'option_file_name' in self.ipopt_options:
raise ValueError(
'Use Ipopt.config.filename to specify the name of the options file. '
'Do not use Ipopt.ipopt_options["option_file_name"].')
for k, v in self.ipopt_options.items():
cmd.append(str(k) + '=' + str(v))
env = os.environ.copy()
if 'PYOMO_AMPLFUNC' in env:
env['AMPLFUNC'] = "\n".join(
filter(None, (env.get('AMPLFUNC',
None), env.get('PYOMO_AMPLFUNC', None))))
with TeeStream(*ostreams) as t:
timer.start('subprocess')
cp = subprocess.run(cmd,
timeout=timeout,
stdout=t.STDOUT,
stderr=t.STDERR,
env=env,
universal_newlines=True)
timer.stop('subprocess')
if cp.returncode != 0:
if self.config.load_solution:
raise RuntimeError(
'A feasible solution was not found, so no solution can be loaded.'
'Please set opt.config.load_solution=False and check '
'results.termination_condition and '
'results.best_feasible_objective before loading a solution.'
)
results = Results()
results.termination_condition = TerminationCondition.error
results.best_feasible_objective = None
self._primal_sol = None
self._dual_sol = None
else:
timer.start('parse solution')
results = self._parse_sol()
timer.stop('parse solution')
if self._writer.get_active_objective() is None:
results.best_objective_bound = None
else:
if self._writer.get_active_objective().sense == minimize:
results.best_objective_bound = -math.inf
else:
results.best_objective_bound = math.inf
return results
def get_primals(
self,
vars_to_load: Optional[Sequence[_GeneralVarData]] = None
) -> Mapping[_GeneralVarData, float]:
res = ComponentMap()
if vars_to_load is None:
for v, val in self._primal_sol.items():
res[v] = val
else:
for v in vars_to_load:
res[v] = self._primal_sol[v]
return res
def get_duals(self, cons_to_load=None):
if cons_to_load is None:
return {k: v for k, v in self._dual_sol.items()}
else:
return {c: self._dual_sol[c] for c in cons_to_load}
def get_reduced_costs(
self,
vars_to_load: Optional[Sequence[_GeneralVarData]] = None
) -> Mapping[_GeneralVarData, float]:
if vars_to_load is None:
return ComponentMap((k, v) for k, v in self._reduced_costs.items())
else:
return ComponentMap(
(v, self._reduced_costs[v]) for v in vars_to_load)