def _build_equality_set(model):
"""Construct an equality set map.
Maps all variables to the set of variables that are linked to them by
equality. Mapping takes place using id(). That is, if you have x = y, then
you would have id(x) -> ComponentSet([x, y]) and id(y) -> ComponentSet([x,
y]) in the mapping.
"""
# Map of variables to their equality set (ComponentSet)
eq_var_map = ComponentMap()
# Loop through all the active constraints in the model
for constraint in model.component_data_objects(
ctype=Constraint, active=True, descend_into=True):
eq_linked_vars = _get_equality_linked_variables(constraint)
if not eq_linked_vars:
continue # if we get an empty tuple, skip to next constraint.
v1, v2 = eq_linked_vars
set1 = eq_var_map.get(v1, ComponentSet((v1, v2)))
set2 = eq_var_map.get(v2, (v2,))
# if set1 and set2 are equivalent, skip to next constraint.
if set1 is set2:
continue
# add all elements of set2 to set 1
set1.update(set2)
# Update all elements to point to set 1
for v in set1:
eq_var_map[v] = set1
return eq_var_map
def __init__(self, **kwds):
# Suffix type information
self._direction = None
self._datatype = None
self._rule = None
# The suffix direction
direction = kwds.pop('direction', Suffix.LOCAL)
# The suffix datatype
datatype = kwds.pop('datatype', Suffix.FLOAT)
# The suffix construction rule
# TODO: deprecate the use of 'rule'
self._rule = kwds.pop('rule', None)
self._rule = kwds.pop('initialize', self._rule)
# Check that keyword values make sense (these function have
# internal error checking).
self.set_direction(direction)
self.set_datatype(datatype)
# Initialize base classes
kwds.setdefault('ctype', Suffix)
ActiveComponent.__init__(self, **kwds)
ComponentMap.__init__(self)
if self._rule is None:
self.construct()
def __init__(self, expression, improved_var_bounds=None):
super(MCPP_visitor, self).__init__()
self.mcpp = _MCPP_lib()
so_file_version = self.mcpp.get_version()
if six.PY3:
so_file_version = so_file_version.decode("utf-8")
if not so_file_version == __version__:
raise MCPP_Error(
"Shared object file version %s is out of date with MC++ interface version %s. "
"Please rebuild the library." % (so_file_version, __version__))
self.missing_value_warnings = []
self.expr = expression
vars = list(identify_variables(expression, include_fixed=False))
self.num_vars = len(vars)
# Map expression variables to MC variables
self.known_vars = ComponentMap()
# Map expression variables to their index
self.var_to_idx = ComponentMap()
# Pre-register all variables
inf = float('inf')
for i, var in enumerate(vars):
self.var_to_idx[var] = i
# check if improved variable bound is provided
if improved_var_bounds is not None:
lb, ub = improved_var_bounds.get(var, (-inf, inf))
else:
lb, ub = -inf, inf
self.known_vars[var] = self.register_var(var, lb, ub)
self.refs = None
def __setstate__(self, state):
"""
This method must be defined for deepcopy/pickling because this
class relies on component ids.
"""
ActiveComponent.__setstate__(self, state)
ComponentMap.__setstate__(self, state)
def _bilinear_expressions(model):
# TODO for now, we look for only expressions where the bilinearities are
# exposed on the root level SumExpression, and thus accessible via
# generate_standard_repn. This will not detect exp(x*y). We require a
# factorization transformation to be applied beforehand in order to pick
# these constraints up.
pass
# Bilinear map will be stored in the format:
# x --> (y --> [constr1, constr2, ...], z --> [constr2, constr3])
bilinear_map = ComponentMap()
for constr in model.component_data_objects(
Constraint, active=True, descend_into=(Block, Disjunct)):
if constr.body.polynomial_degree() in (1, 0):
continue # Skip trivial and linear constraints
repn = generate_standard_repn(constr.body)
for pair in repn.quadratic_vars:
v1, v2 = pair
v1_pairs = bilinear_map.get(v1, ComponentMap())
if v2 in v1_pairs:
# bilinear term has been found before. Simply add constraint to
# the set associated with the bilinear term.
v1_pairs[v2].add(constr)
else:
# We encounter the bilinear term for the first time.
bilinear_map[v1] = v1_pairs
bilinear_map[v2] = bilinear_map.get(v2, ComponentMap())
constraints_with_bilinear_pair = ComponentSet([constr])
bilinear_map[v1][v2] = constraints_with_bilinear_pair
bilinear_map[v2][v1] = constraints_with_bilinear_pair
return bilinear_map
def __setstate__(self, state):
"""
This method must be defined for deepcopy/pickling because this
class relies on component ids.
"""
ActiveComponent.__setstate__(self, state)
ComponentMap.__setstate__(self, state)
def _set_instance(self, model, kwds={}):
self._range_constraints = set()
DirectOrPersistentSolver._set_instance(self, model, kwds)
self._pyomo_con_to_solver_con_map = dict()
self._solver_con_to_pyomo_con_map = ComponentMap()
self._pyomo_var_to_solver_var_map = ComponentMap()
self._solver_var_to_pyomo_var_map = ComponentMap()
try:
if model.name is not None:
self._solver_model = self._gurobipy.Model(model.name)
else:
self._solver_model = self._gurobipy.Model()
except Exception:
e = sys.exc_info()[1]
msg = (
'Unable to create Gurobi model. Have you installed the Python bindings for Gurboi?\n\n\t'
+ 'Error message: {0}'.format(e))
raise Exception(msg)
self._add_block(model)
for var, n_ref in self._referenced_variables.items():
if n_ref != 0:
if var.fixed:
if not self._output_fixed_variable_bounds:
raise ValueError(
"Encountered a fixed variable (%s) inside an active objective "
"or constraint expression on model %s, which is usually indicative of "
"a preprocessing error. Use the IO-option 'output_fixed_variable_bounds=True' "
"to suppress this error and fix the variable by overwriting its bounds in "
"the Gurobi instance." % (
var.name,
self._pyomo_model.name,
))
def _apply_to(self, instance, **kwds):
config = self.CONFIG(kwds)
if config.reversible:
if any(
hasattr(instance, map_name) for map_name in [
'_tmp_var_bound_strip_lb', '_tmp_var_bound_strip_ub',
'_tmp_var_bound_strip_domain'
]):
raise RuntimeError(
'Variable stripping reversion component maps already '
'exist. Did you already apply a temporary transformation '
'without a subsequent reversion?')
# Component maps to store data for reversion.
instance._tmp_var_bound_strip_lb = ComponentMap()
instance._tmp_var_bound_strip_ub = ComponentMap()
instance._tmp_var_bound_strip_domain = ComponentMap()
for var in instance.component_data_objects(ctype=Var):
if config.strip_domains and not var.domain == Reals:
if config.reversible:
instance._tmp_var_bound_strip_domain[var] = var.domain
var.domain = Reals
if var.has_lb():
if config.reversible:
instance._tmp_var_bound_strip_lb[var] = var.lb
var.setlb(None)
if var.has_ub():
if config.reversible:
instance._tmp_var_bound_strip_ub[var] = var.ub
var.setub(None)
def test_clear(self):
cmap = ComponentMap()
self.assertEqual(len(cmap), 0)
cmap.update(self._components)
self.assertEqual(len(cmap), len(self._components))
cmap.clear()
self.assertEqual(len(cmap), 0)
def _build_equality_set(model):
"""Construct an equality set map.
Maps all variables to the set of variables that are linked to them by
equality. Mapping takes place using id(). That is, if you have x = y, then
you would have id(x) -> ComponentSet([x, y]) and id(y) -> ComponentSet([x,
y]) in the mapping.
"""
# Map of variables to their equality set (ComponentSet)
eq_var_map = ComponentMap()
# Loop through all the active constraints in the model
for constraint in model.component_data_objects(ctype=Constraint,
active=True,
descend_into=True):
eq_linked_vars = _get_equality_linked_variables(constraint)
if not eq_linked_vars:
continue # if we get an empty tuple, skip to next constraint.
v1, v2 = eq_linked_vars
set1 = eq_var_map.get(v1, ComponentSet((v1, v2)))
set2 = eq_var_map.get(v2, (v2, ))
# if set1 and set2 are equivalent, skip to next constraint.
if set1 is set2:
continue
# add all elements of set2 to set 1
set1.update(set2)
# Update all elements to point to set 1
for v in set1:
eq_var_map[v] = set1
return eq_var_map
def _set_instance(self, model, kwds={}):
if not isinstance(model, (Model, IBlockStorage)):
msg = "The problem instance supplied to the {0} plugin " \
"'_presolve' method must be of type 'Model'".format(type(self))
raise ValueError(msg)
self._pyomo_model = model
self._symbolic_solver_labels = kwds.pop('symbolic_solver_labels',
self._symbolic_solver_labels)
self._skip_trivial_constraints = kwds.pop(
'skip_trivial_constraints', self._skip_trivial_constraints)
self._output_fixed_variable_bounds = kwds.pop(
'output_fixed_variable_bounds', self._output_fixed_variable_bounds)
self._pyomo_var_to_solver_var_map = ComponentMap()
self._pyomo_con_to_solver_con_map = ComponentMap()
self._vars_referenced_by_con = ComponentMap()
self._vars_referenced_by_obj = ComponentSet()
self._referenced_variables = ComponentMap()
self._objective_label = None
self._objective = None
self._symbol_map = SymbolMap()
if self._symbolic_solver_labels:
self._labeler = TextLabeler()
else:
self._labeler = NumericLabeler('x')
def _get_constraint_map_dict(self, transBlock):
if not hasattr(transBlock, "_constraintMap"):
transBlock._constraintMap = {
'srcConstraints': ComponentMap(),
'transformedConstraints': ComponentMap()
}
return transBlock._constraintMap
def test_update(self):
cmap = ComponentMap()
self.assertEqual(len(cmap), 0)
cmap.update(self._components)
self.assertEqual(len(cmap), len(self._components))
for c, val in self._components:
self.assertEqual(cmap[c], val)
def _build_equality_set(m):
"""Construct an equality set map.
Maps all variables to the set of variables that are linked to them by
equality. Mapping takes place using id(). That is, if you have x = y, then
you would have id(x) -> ComponentSet([x, y]) and id(y) -> ComponentSet([x,
y]) in the mapping.
"""
#: dict: map of var UID to the set of all equality-linked var UIDs
eq_var_map = ComponentMap()
relevant_vars = ComponentSet()
for constr in m.component_data_objects(ctype=Constraint,
active=True,
descend_into=True):
# Check to make sure the constraint is of form v1 - v2 == 0
if (value(constr.lower) == 0 and value(constr.upper) == 0
and constr.body.polynomial_degree() == 1):
repn = generate_canonical_repn(constr.body)
# only take the variables with nonzero coefficients
vars_ = [v for i, v in enumerate(repn.variables) if repn.linear[i]]
if (len(vars_) == 2
and sorted(l for l in repn.linear if l) == [-1, 1]):
# this is an a == b constraint.
v1 = vars_[0]
v2 = vars_[1]
set1 = eq_var_map.get(v1, ComponentSet([v1]))
set2 = eq_var_map.get(v2, ComponentSet([v2]))
relevant_vars.update([v1, v2])
set1.update(set2) # set1 is now the union
for v in set1:
eq_var_map[v] = set1
return eq_var_map, relevant_vars
def __init__(self, **kwds):
# Suffix type information
self._direction = None
self._datatype = None
self._rule = None
# The suffix direction
direction = kwds.pop('direction', Suffix.LOCAL)
# The suffix datatype
datatype = kwds.pop('datatype', Suffix.FLOAT)
# The suffix construction rule
# TODO: deprecate the use of 'rule'
self._rule = kwds.pop('rule', None)
self._rule = kwds.pop('initialize', self._rule)
# Check that keyword values make sense (these function have
# internal error checking).
self.set_direction(direction)
self.set_datatype(datatype)
# Initialize base classes
kwds.setdefault('ctype', Suffix)
ActiveComponent.__init__(self, **kwds)
ComponentMap.__init__(self)
if self._rule is None:
self.construct()
def test_update(self):
cmap = ComponentMap()
self.assertEqual(len(cmap), 0)
cmap.update(self._components)
self.assertEqual(len(cmap), len(self._components))
for c, val in self._components:
self.assertEqual(cmap[c], val)
def _build_equality_set(m):
"""Construct an equality set map.
Maps all variables to the set of variables that are linked to them by
equality. Mapping takes place using id(). That is, if you have x = y, then
you would have id(x) -> ComponentSet([x, y]) and id(y) -> ComponentSet([x,
y]) in the mapping.
"""
#: dict: map of var UID to the set of all equality-linked var UIDs
eq_var_map = ComponentMap()
relevant_vars = ComponentSet()
for constr in m.component_data_objects(ctype=Constraint,
active=True,
descend_into=True):
# Check to make sure the constraint is of form v1 - v2 == 0
if (value(constr.lower) == 0 and value(constr.upper) == 0 and
constr.body.polynomial_degree() == 1):
repn = generate_standard_repn(constr.body)
# only take the variables with nonzero coefficients
vars_ = [v for i, v in enumerate(repn.linear_vars)
if repn.linear_coefs[i]]
if (len(vars_) == 2 and
sorted(l for l in repn.linear_coefs if l) == [-1, 1]):
# this is an a == b constraint.
v1 = vars_[0]
v2 = vars_[1]
set1 = eq_var_map.get(v1, ComponentSet([v1]))
set2 = eq_var_map.get(v2, ComponentSet([v2]))
relevant_vars.update([v1, v2])
set1.update(set2) # set1 is now the union
for v in set1:
eq_var_map[v] = set1
return eq_var_map, relevant_vars
def reverse_sd(expr):
"""
First order reverse ad
Parameters
----------
expr: pyomo.core.expr.numeric_expr.ExpressionBase
expression to differentiate
Returns
-------
pyomo.core.kernel.component_map.ComponentMap
component_map mapping variables to derivatives with respect
to the corresponding variable
"""
val_dict = ComponentMap()
der_dict = ComponentMap()
visitorA = _ReverseSDVisitorLeafToRoot(val_dict, der_dict)
visitorA.dfs_postorder_stack(expr)
der_dict[expr] = 1
visitorB = _ReverseSDVisitorRootToLeaf(val_dict, der_dict)
visitorB.dfs_postorder_stack(expr)
return der_dict
def test_items(self):
cmap = ComponentMap(self._components)
for x in cmap.items():
self.assertEqual(type(x), tuple)
self.assertEqual(len(x), 2)
self.assertEqual(
sorted(cmap.items(), key=lambda _x: (id(_x[0]), _x[1])),
sorted(self._components, key=lambda _x: (id(_x[0]), _x[1])))
def test_len(self):
cmap = ComponentMap()
self.assertEqual(len(cmap), 0)
cmap.update(self._components)
self.assertEqual(len(cmap), len(self._components))
cmap = ComponentMap(self._components)
self.assertEqual(len(cmap), len(self._components))
self.assertTrue(len(self._components) > 0)
def __init__(self, component):
BaseRelaxationData.__init__(self, component)
self._partitions = ComponentMap()
"""ComponentMap: var: list of float"""
self._saved_partitions = []
"""list of CompnentMap"""
def test_iter(self):
cmap = ComponentMap()
self.assertEqual(list(iter(cmap)), [])
cmap.update(self._components)
ids_seen = set()
for c in cmap:
ids_seen.add(id(c))
self.assertEqual(ids_seen, set(id(c) for c, val in self._components))
def __init__(self, **kwds):
kwds['type'] = 'gurobi_direct'
DirectSolver.__init__(self, **kwds)
self._pyomo_var_to_solver_var_map = ComponentMap()
self._solver_var_to_pyomo_var_map = ComponentMap()
self._pyomo_con_to_solver_con_map = dict()
self._solver_con_to_pyomo_con_map = ComponentMap()
self._init()
def test_product_with_itself(self, visitor, f, cvx_f, bounds_f, expected):
convexity = ComponentMap()
convexity[f] = cvx_f
bounds = ComponentMap()
bounds[f] = bounds_f
expr = f * f
matched, result = visitor.visit_expression(expr, convexity, None, bounds)
assert matched
assert result == expected
def _result_with_mono_bounds(self, visitor, g, mono_g, bounds_g):
mono = ComponentMap()
mono[g] = mono_g
bounds = ComponentMap()
bounds[g] = bounds_g
expr = pe.cos(g)
matched, result = visitor.visit_expression(expr, mono, bounds)
assert matched
return result
def test_items(self):
cmap = ComponentMap(self._components)
for x in cmap.items():
self.assertEqual(type(x), tuple)
self.assertEqual(len(x), 2)
self.assertEqual(sorted(cmap.items(),
key=lambda _x: (id(_x[0]), _x[1])),
sorted(self._components,
key=lambda _x: (id(_x[0]), _x[1])))
def test_iter(self):
cmap = ComponentMap()
self.assertEqual(list(iter(cmap)), [])
cmap.update(self._components)
ids_seen = set()
for c in cmap:
ids_seen.add(id(c))
self.assertEqual(ids_seen,
set(id(c) for c,val in self._components))
def test_abs(visitor, g, mono_g, bounds_g, expected):
bounds = ComponentMap()
bounds[g] = bounds_g
mono = ComponentMap()
mono[g] = mono_g
expr = abs(g)
matched, result = visitor.visit_expression(expr, mono, bounds)
assert matched
assert result == expected
def test_reciprocal(visitor, g, mono_g, bound_g, expected):
bounds = ComponentMap()
bounds[g] = bound_g
mono = ComponentMap()
mono[g] = mono_g
expr = ReciprocalExpression([g])
matched, result = visitor.visit_expression(expr, mono, bounds)
assert matched
assert result == expected
def test_nonincreasing_function(visitor, g, func_name, mono_g, bounds_g):
mono = ComponentMap()
mono[g] = mono_g
bounds = ComponentMap()
bounds[g] = bounds_g
func = getattr(pe, func_name)
expr = func(g)
matched, result = visitor.visit_expression(expr, mono, bounds)
assert matched
assert result == mono_g.negate()
def test_negation(visitor, g, mono_g, bounds_g):
mono = ComponentMap()
mono[g] = mono_g
bounds = ComponentMap()
bounds[g] = bounds_g
expr = -g
assume(isinstance(expr, NegationExpression))
matched, result = visitor.visit_expression(expr, mono, bounds)
assert matched
assert result == mono_g.negate()
def __init__(self):
self.pyomo2sympy = ComponentMap()
self.parent_symbol = ComponentMap()
self.sympy2pyomo = {}
self.sympy2latex = {}
self.used = {}
self.i_var = 0
self.i_expr = 0
self.i_func = 0
self.i = 0
def generate_names(self,
active=None,
descend_into=True,
convert=str,
prefix=""):
"""
Generate a container of fully qualified names (up to
this container) for objects stored under this
container.
Args:
active (:const:`True`/:const:`None`): Set to
:const:`True` to indicate that only active
components should be included. The default
value of :const:`None` indicates that all
components (including those that have been
deactivated) should be included. *Note*:
This flag is ignored for any objects that do
not have an active flag.
descend_into (bool): Indicates whether or not to
include subcomponents of any container
objects that are not components. Default is
:const:`True`.
convert (function): A function that converts a
storage key into a string
representation. Default is str.
prefix (str): A string to prefix names with.
Returns:
A component map that behaves as a dictionary
mapping component objects to names.
"""
assert active in (None, True)
from pyomo.core.kernel.component_map import ComponentMap
names = ComponentMap()
# if not active, then no children can be active
if (active is not None) and \
not getattr(self, _active_flag_name, True):
return names
name_template = (prefix + self._child_storage_delimiter_string +
self._child_storage_entry_string)
for child in self.children():
if (active is None) or \
getattr(child, _active_flag_name, True):
names[child] = (name_template % convert(child.storage_key))
if descend_into and child._is_container and \
(not child._is_component):
names.update(
child.generate_names(active=active,
descend_into=True,
convert=convert,
prefix=names[child]))
return names
def _result_with_base_expo(self, visitor, base, mono_base, bounds_base, expo):
mono = ComponentMap()
mono[base] = mono_base
mono[expo] = M.Constant
bounds = ComponentMap()
bounds[base] = bounds_base
bounds[expo] = I(expo, expo)
expr = PowExpression([base, expo])
matched, result = visitor.visit_expression(expr, mono, bounds)
assert matched
return result
def test_pow(visitor, base, expo, mono_base, bounds_base, mono_expo, bounds_expo):
mono = ComponentMap()
mono[base] = mono_base
mono[expo] = mono_expo
bounds = ComponentMap()
bounds[base] = bounds_base
bounds[expo] = bounds_expo
expr = PowExpression([base, expo])
matched, result = visitor.visit_expression(expr, mono, bounds)
assert matched
assert result == M.Unknown
def __init__(self, **kwds):
if 'type' not in kwds:
kwds['type'] = 'gurobi_direct'
super(GurobiDirect, self).__init__(**kwds)
self._pyomo_var_to_solver_var_map = ComponentMap()
self._solver_var_to_pyomo_var_map = ComponentMap()
self._pyomo_con_to_solver_con_map = dict()
self._solver_con_to_pyomo_con_map = ComponentMap()
self._needs_updated = True # flag that indicates if solver_model.update() needs called before getting variable and constraint attributes
self._callback = None
self._callback_func = None
self._name = None
try:
import gurobipy
self._gurobipy = gurobipy
self._python_api_exists = True
self._version = self._gurobipy.gurobi.version()
self._name = "Gurobi %s.%s%s" % self._version
while len(self._version) < 4:
self._version += (0, )
self._version = self._version[:4]
self._version_major = self._version[0]
except ImportError:
self._python_api_exists = False
except Exception as e:
# other forms of exceptions can be thrown by the gurobi python
# import. for example, a gurobipy.GurobiError exception is thrown
# if all tokens for Gurobi are already in use. assuming, of
# course, the license is a token license. unfortunately, you can't
# import without a license, which means we can't test for the
# exception above!
print("Import of gurobipy failed - gurobi message=" + str(e) +
"\n")
self._python_api_exists = False
self._range_constraints = set()
self._max_obj_degree = 2
self._max_constraint_degree = 2
# Note: Undefined capabilites default to None
self._capabilities.linear = True
self._capabilities.quadratic_objective = True
self._capabilities.quadratic_constraint = True
self._capabilities.integer = True
self._capabilities.sos1 = True
self._capabilities.sos2 = True
# fix for compatibility with pre-5.0 Gurobi
if self._python_api_exists and \
(self._version_major < 5):
self._max_constraint_degree = 1
self._capabilities.quadratic_constraint = False
def _rule_result(self, visitor, g, cvx, mono_g, bounds_g, func):
convexity = ComponentMap()
convexity[g] = cvx
mono = ComponentMap()
mono[g] = mono_g
bounds = ComponentMap()
bounds[g] = bounds_g
expr = func(g)
matched, result = visitor.visit_expression(expr, convexity, mono, bounds)
assert matched
return result
def test_visitor_tightens_new_bounds(visitor, expr):
bounds = ComponentMap()
assert bounds.get(expr, None) is None
assert visitor.handle_result(expr, Interval(0, 2), bounds)
assert bounds[expr] == Interval(0, 2)
assert not visitor.handle_result(expr, Interval(0, 2), bounds)
assert bounds[expr] == Interval(0, 2)
assert visitor.handle_result(expr, Interval(0, 1), bounds)
assert bounds[expr] == Interval(0, 1)
def calc_jacobians(solve_data, config):
"""Generate a map of jacobians."""
# Map nonlinear_constraint --> Map(
# variable --> jacobian of constraint wrt. variable)
solve_data.jacobians = ComponentMap()
for c in solve_data.mip.MindtPy_utils.constraint_list:
if c.body.polynomial_degree() in (1, 0):
continue # skip linear constraints
vars_in_constr = list(EXPR.identify_variables(c.body))
jac_list = differentiate(c.body, wrt_list=vars_in_constr)
solve_data.jacobians[c] = ComponentMap(
(var, jac_wrt_var)
for var, jac_wrt_var in zip(vars_in_constr, jac_list))
def _result_with_base_expo(self, visitor, base, expo, mono_expo, bounds_expo):
rule = PowerRule()
mono = ComponentMap()
mono[base] = M.Constant
mono[expo] = mono_expo
bounds = ComponentMap()
bounds[base] = I(base, base)
bounds[expo] = bounds_expo
expr = base ** expo
assume(isinstance(expr, PowExpression))
matched, result = visitor.visit_expression(expr, mono, bounds)
assert matched
return result
def test_getsetdelitem(self):
cmap = ComponentMap()
for c, val in self._components:
self.assertTrue(c not in cmap)
for c, val in self._components:
cmap[c] = val
self.assertEqual(cmap[c], val)
self.assertEqual(cmap.get(c), val)
del cmap[c]
with self.assertRaises(KeyError):
cmap[c]
with self.assertRaises(KeyError):
del cmap[c]
self.assertEqual(cmap.get(c), None)
def determine_valid_values(block, discr_var_to_constrs_map, config):
"""Calculate valid values for each effectively discrete variable.
We need the set of possible values for the effectively discrete variable in
order to do the reformulations.
Right now, we select a naive approach where we look for variables in the
discreteness-inducing constraints. We then adjust their values and see if
things are stil feasible. Based on their coefficient values, we can infer a
set of allowable values for the effectively discrete variable.
Args:
block: The model or a disjunct on the model.
"""
possible_values = ComponentMap()
for eff_discr_var, constrs in discr_var_to_constrs_map.items():
# get the superset of possible values by looking through the
# constraints
for constr in constrs:
repn = generate_standard_repn(constr.body)
var_coef = sum(coef for i, coef in enumerate(repn.linear_coefs)
if repn.linear_vars[i] is eff_discr_var)
const = -(repn.constant - constr.upper) / var_coef
possible_vals = set((const,))
for i, var in enumerate(repn.linear_vars):
if var is eff_discr_var:
continue
coef = -repn.linear_coefs[i] / var_coef
if var.is_binary():
var_values = (0, coef)
elif var.is_integer():
var_values = [v * coef for v in range(var.lb, var.ub + 1)]
else:
raise ValueError(
'%s has unacceptable variable domain: %s' %
(var.name, var.domain))
possible_vals = set(
(v1 + v2 for v1 in possible_vals for v2 in var_values))
old_possible_vals = possible_values.get(eff_discr_var, None)
if old_possible_vals is not None:
possible_values[eff_discr_var] = old_possible_vals & possible_vals
else:
possible_values[eff_discr_var] = possible_vals
possible_values = prune_possible_values(block, possible_values, config)
return possible_values
def test_setdefault(self):
cmap = ComponentMap()
for c,_ in self._components:
with self.assertRaises(KeyError):
cmap[c]
self.assertTrue(c not in cmap)
cmap.setdefault(c, []).append(1)
self.assertEqual(cmap[c], [1])
del cmap[c]
with self.assertRaises(KeyError):
cmap[c]
self.assertTrue(c not in cmap)
cmap[c] = []
cmap.setdefault(c, []).append(1)
self.assertEqual(cmap[c], [1])
def _set_instance(self, model, kwds={}):
self._range_constraints = set()
DirectOrPersistentSolver._set_instance(self, model, kwds)
self._pyomo_con_to_solver_con_map = dict()
self._solver_con_to_pyomo_con_map = ComponentMap()
self._pyomo_var_to_solver_var_map = ComponentMap()
self._solver_var_to_pyomo_var_map = ComponentMap()
try:
if model.name is not None:
self._solver_model = self._gurobipy.Model(model.name)
else:
self._solver_model = self._gurobipy.Model()
except Exception:
e = sys.exc_info()[1]
msg = ("Unable to create Gurobi model. "
"Have you installed the Python "
"bindings for Gurboi?\n\n\t"+
"Error message: {0}".format(e))
raise Exception(msg)
self._add_block(model)
for var, n_ref in self._referenced_variables.items():
if n_ref != 0:
if var.fixed:
if not self._output_fixed_variable_bounds:
raise ValueError(
"Encountered a fixed variable (%s) inside "
"an active objective or constraint "
"expression on model %s, which is usually "
"indicative of a preprocessing error. Use "
"the IO-option 'output_fixed_variable_bounds=True' "
"to suppress this error and fix the variable "
"by overwriting its bounds in the Gurobi instance."
% (var.name, self._pyomo_model.name,))
def __init__(self, **kwds):
kwds['type'] = 'gurobi_direct'
DirectSolver.__init__(self, **kwds)
self._pyomo_var_to_solver_var_map = ComponentMap()
self._solver_var_to_pyomo_var_map = ComponentMap()
self._pyomo_con_to_solver_con_map = dict()
self._solver_con_to_pyomo_con_map = ComponentMap()
self._init()
def test_eq(self):
cmap1 = ComponentMap()
self.assertNotEqual(cmap1, set())
self.assertFalse(cmap1 == set())
self.assertNotEqual(cmap1, list())
self.assertFalse(cmap1 == list())
self.assertNotEqual(cmap1, tuple())
self.assertFalse(cmap1 == tuple())
self.assertEqual(cmap1, dict())
self.assertTrue(cmap1 == dict())
cmap1.update(self._components)
self.assertNotEqual(cmap1, set())
self.assertFalse(cmap1 == set())
self.assertNotEqual(cmap1, list())
self.assertFalse(cmap1 == list())
self.assertNotEqual(cmap1, tuple())
self.assertFalse(cmap1 == tuple())
self.assertNotEqual(cmap1, dict())
self.assertFalse(cmap1 == dict())
self.assertTrue(cmap1 == cmap1)
self.assertEqual(cmap1, cmap1)
cmap2 = ComponentMap(self._components)
self.assertTrue(cmap2 == cmap1)
self.assertFalse(cmap2 != cmap1)
self.assertEqual(cmap2, cmap1)
self.assertTrue(cmap1 == cmap2)
self.assertFalse(cmap1 != cmap2)
self.assertEqual(cmap1, cmap2)
del cmap2[self._components[0][0]]
self.assertFalse(cmap2 == cmap1)
self.assertTrue(cmap2 != cmap1)
self.assertNotEqual(cmap2, cmap1)
self.assertFalse(cmap1 == cmap2)
self.assertTrue(cmap1 != cmap2)
self.assertNotEqual(cmap1, cmap2)
def detect_effectively_discrete_vars(block, equality_tolerance):
"""Detect effectively discrete variables.
These continuous variables are the sum of discrete variables.
"""
# Map of effectively_discrete var --> inducing constraints
effectively_discrete = ComponentMap()
for constr in block.component_data_objects(Constraint, active=True):
if constr.lower is None or constr.upper is None:
continue # skip inequality constraints
if fabs(value(constr.lower) - value(constr.upper)
) > equality_tolerance:
continue # not equality constriant. Skip.
if constr.body.polynomial_degree() not in (1, 0):
continue # skip nonlinear expressions
repn = generate_standard_repn(constr.body)
if len(repn.linear_vars) < 2:
# TODO should this be < 2 or < 1?
# TODO we should make sure that trivial equality relations are
# preprocessed before this, or we will end up reformulating
# expressions that we do not need to here.
continue
non_discrete_vars = list(v for v in repn.linear_vars
if v.is_continuous())
if len(non_discrete_vars) == 1:
# We know that this is an effectively discrete continuous
# variable. Add it to our identified variable list.
var = non_discrete_vars[0]
inducing_constraints = effectively_discrete.get(var, [])
inducing_constraints.append(constr)
effectively_discrete[var] = inducing_constraints
# TODO we should eventually also look at cases where all other
# non_discrete_vars are effectively_discrete_vars
return effectively_discrete
def __init__(self, component=None):
#
# These lines represent in-lining of the
# following constructors:
# - ComponentData
# - NumericValue
self._component = weakref_ref(component) if (component is not None) \
else None
self.vars = {}
self._arcs = []
self._sources = []
self._dests = []
self._rules = {}
self._splitfracs = ComponentMap()
def fbbt(comp, deactivate_satisfied_constraints=False, integer_tol=1e-5, infeasible_tol=1e-8):
"""
Perform FBBT on a constraint, block, or model. For more control,
use fbbt_con and fbbt_block. For detailed documentation, see
the docstrings for fbbt_con and fbbt_block.
Parameters
----------
comp: pyomo.core.base.constraint.Constraint or pyomo.core.base.block.Block or pyomo.core.base.PyomoModel.ConcreteModel
deactivate_satisfied_constraints: bool
If deactivate_satisfied_constraints is True and a constraint is always satisfied, then the constranit
will be deactivated
integer_tol: float
If the lower bound computed on a binary variable is less than or equal to integer_tol, then the
lower bound is left at 0. Otherwise, the lower bound is increased to 1. If the upper bound computed
on a binary variable is greater than or equal to 1-integer_tol, then the upper bound is left at 1.
Otherwise the upper bound is decreased to 0.
infeasible_tol: float
If the bounds computed on the body of a constraint violate the bounds of the constraint by more than
infeasible_tol, then the constraint is considered infeasible and an exception is raised.
Returns
-------
new_var_bounds: ComponentMap
A ComponentMap mapping from variables a tuple containing the lower and upper bounds, respectively, computed
from FBBT.
"""
new_var_bounds = ComponentMap()
if comp.type() == Constraint:
if comp.is_indexed():
for _c in comp.values():
_new_var_bounds = fbbt_con(comp, deactivate_satisfied_constraints=deactivate_satisfied_constraints,
integer_tol=integer_tol, infeasible_tol=infeasible_tol)
new_var_bounds.update(_new_var_bounds)
else:
_new_var_bounds = fbbt_con(comp, deactivate_satisfied_constraints=deactivate_satisfied_constraints,
integer_tol=integer_tol, infeasible_tol=infeasible_tol)
new_var_bounds.update(_new_var_bounds)
elif comp.type() == Block:
_new_var_bounds = fbbt_block(comp, deactivate_satisfied_constraints=deactivate_satisfied_constraints,
integer_tol=integer_tol, infeasible_tol=infeasible_tol)
new_var_bounds.update(_new_var_bounds)
else:
raise FBBTException('Cannot perform FBBT on objects of type {0}'.format(type(comp)))
return new_var_bounds
class GurobiDirect(DirectSolver):
def __init__(self, **kwds):
kwds['type'] = 'gurobi_direct'
DirectSolver.__init__(self, **kwds)
self._pyomo_var_to_solver_var_map = ComponentMap()
self._solver_var_to_pyomo_var_map = ComponentMap()
self._pyomo_con_to_solver_con_map = dict()
self._solver_con_to_pyomo_con_map = ComponentMap()
self._init()
def _init(self):
self._name = None
try:
import gurobipy
self._gurobipy = gurobipy
self._python_api_exists = True
self._version = self._gurobipy.gurobi.version()
self._name = "Gurobi %s.%s%s" % self._version
while len(self._version) < 4:
self._version += (0,)
self._version = self._version[:4]
self._version_major = self._version[0]
except ImportError:
self._python_api_exists = False
except Exception as e:
# other forms of exceptions can be thrown by the gurobi python
# import. for example, a gurobipy.GurobiError exception is thrown
# if all tokens for Gurobi are already in use. assuming, of
# course, the license is a token license. unfortunately, you can't
# import without a license, which means we can't test for the
# exception above!
print("Import of gurobipy failed - gurobi message=" + str(e) + "\n")
self._python_api_exists = False
self._range_constraints = set()
self._max_obj_degree = 2
self._max_constraint_degree = 2
# Note: Undefined capabilites default to None
self._capabilities.linear = True
self._capabilities.quadratic_objective = True
self._capabilities.quadratic_constraint = True
self._capabilities.integer = True
self._capabilities.sos1 = True
self._capabilities.sos2 = True
# fix for compatibility with pre-5.0 Gurobi
if self._python_api_exists and \
(self._version_major < 5):
self._max_constraint_degree = 1
self._capabilities.quadratic_constraint = False
def _apply_solver(self):
if not self._save_results:
for block in self._pyomo_model.block_data_objects(descend_into=True,
active=True):
for var in block.component_data_objects(ctype=pyomo.core.base.var.Var,
descend_into=False,
active=True,
sort=False):
var.stale = True
if self._tee:
self._solver_model.setParam('OutputFlag', 1)
else:
self._solver_model.setParam('OutputFlag', 0)
self._solver_model.setParam('LogFile', self._log_file)
if self._keepfiles:
print("Solver log file: "+self._log_file)
# Options accepted by gurobi (case insensitive):
# ['Cutoff', 'IterationLimit', 'NodeLimit', 'SolutionLimit', 'TimeLimit',
# 'FeasibilityTol', 'IntFeasTol', 'MarkowitzTol', 'MIPGap', 'MIPGapAbs',
# 'OptimalityTol', 'PSDTol', 'Method', 'PerturbValue', 'ObjScale', 'ScaleFlag',
# 'SimplexPricing', 'Quad', 'NormAdjust', 'BarIterLimit', 'BarConvTol',
# 'BarCorrectors', 'BarOrder', 'Crossover', 'CrossoverBasis', 'BranchDir',
# 'Heuristics', 'MinRelNodes', 'MIPFocus', 'NodefileStart', 'NodefileDir',
# 'NodeMethod', 'PumpPasses', 'RINS', 'SolutionNumber', 'SubMIPNodes', 'Symmetry',
# 'VarBranch', 'Cuts', 'CutPasses', 'CliqueCuts', 'CoverCuts', 'CutAggPasses',
# 'FlowCoverCuts', 'FlowPathCuts', 'GomoryPasses', 'GUBCoverCuts', 'ImpliedCuts',
# 'MIPSepCuts', 'MIRCuts', 'NetworkCuts', 'SubMIPCuts', 'ZeroHalfCuts', 'ModKCuts',
# 'Aggregate', 'AggFill', 'PreDual', 'DisplayInterval', 'IISMethod', 'InfUnbdInfo',
# 'LogFile', 'PreCrush', 'PreDepRow', 'PreMIQPMethod', 'PrePasses', 'Presolve',
# 'ResultFile', 'ImproveStartTime', 'ImproveStartGap', 'Threads', 'Dummy', 'OutputFlag']
for key, option in self.options.items():
# When options come from the pyomo command, all
# values are string types, so we try to cast
# them to a numeric value in the event that
# setting the parameter fails.
try:
self._solver_model.setParam(key, option)
except TypeError:
# we place the exception handling for
# checking the cast of option to a float in
# another function so that we can simply
# call raise here instead of except
# TypeError as e / raise e, because the
# latter does not preserve the Gurobi stack
# trace
if not _is_numeric(option):
raise
self._solver_model.setParam(key, float(option))
if self._version_major >= 5:
for suffix in self._suffixes:
if re.match(suffix, "dual"):
self._solver_model.setParam(self._gurobipy.GRB.Param.QCPDual, 1)
self._solver_model.optimize()
self._solver_model.setParam('LogFile', 'default')
# FIXME: can we get a return code indicating if Gurobi had a significant failure?
return Bunch(rc=None, log=None)
def _get_expr_from_pyomo_repn(self, repn, max_degree=2):
referenced_vars = ComponentSet()
degree = repn.polynomial_degree()
if (degree is None) or (degree > max_degree):
raise DegreeError('GurobiDirect does not support expressions of degree {0}.'.format(degree))
if len(repn.linear_vars) > 0:
referenced_vars.update(repn.linear_vars)
new_expr = self._gurobipy.LinExpr(repn.linear_coefs, [self._pyomo_var_to_solver_var_map[i] for i in repn.linear_vars])
else:
new_expr = 0.0
for i,v in enumerate(repn.quadratic_vars):
x,y = v
new_expr += repn.quadratic_coefs[i] * self._pyomo_var_to_solver_var_map[x] * self._pyomo_var_to_solver_var_map[y]
referenced_vars.add(x)
referenced_vars.add(y)
new_expr += repn.constant
return new_expr, referenced_vars
def _get_expr_from_pyomo_expr(self, expr, max_degree=2):
if max_degree == 2:
repn = generate_standard_repn(expr, quadratic=True)
else:
repn = generate_standard_repn(expr, quadratic=False)
try:
gurobi_expr, referenced_vars = self._get_expr_from_pyomo_repn(repn, max_degree)
except DegreeError as e:
msg = e.args[0]
msg += '\nexpr: {0}'.format(expr)
raise DegreeError(msg)
return gurobi_expr, referenced_vars
def _add_var(self, var):
varname = self._symbol_map.getSymbol(var, self._labeler)
vtype = self._gurobi_vtype_from_var(var)
if var.has_lb():
lb = value(var.lb)
else:
lb = -self._gurobipy.GRB.INFINITY
if var.has_ub():
ub = value(var.ub)
else:
ub = self._gurobipy.GRB.INFINITY
gurobipy_var = self._solver_model.addVar(lb=lb, ub=ub, vtype=vtype, name=varname)
self._pyomo_var_to_solver_var_map[var] = gurobipy_var
self._solver_var_to_pyomo_var_map[gurobipy_var] = var
self._referenced_variables[var] = 0
if var.is_fixed():
gurobipy_var.setAttr('lb', var.value)
gurobipy_var.setAttr('ub', var.value)
def _set_instance(self, model, kwds={}):
self._range_constraints = set()
DirectOrPersistentSolver._set_instance(self, model, kwds)
self._pyomo_con_to_solver_con_map = dict()
self._solver_con_to_pyomo_con_map = ComponentMap()
self._pyomo_var_to_solver_var_map = ComponentMap()
self._solver_var_to_pyomo_var_map = ComponentMap()
try:
if model.name is not None:
self._solver_model = self._gurobipy.Model(model.name)
else:
self._solver_model = self._gurobipy.Model()
except Exception:
e = sys.exc_info()[1]
msg = ("Unable to create Gurobi model. "
"Have you installed the Python "
"bindings for Gurboi?\n\n\t"+
"Error message: {0}".format(e))
raise Exception(msg)
self._add_block(model)
for var, n_ref in self._referenced_variables.items():
if n_ref != 0:
if var.fixed:
if not self._output_fixed_variable_bounds:
raise ValueError(
"Encountered a fixed variable (%s) inside "
"an active objective or constraint "
"expression on model %s, which is usually "
"indicative of a preprocessing error. Use "
"the IO-option 'output_fixed_variable_bounds=True' "
"to suppress this error and fix the variable "
"by overwriting its bounds in the Gurobi instance."
% (var.name, self._pyomo_model.name,))
def _add_block(self, block):
DirectOrPersistentSolver._add_block(self, block)
self._solver_model.update()
def _add_constraint(self, con):
if not con.active:
return None
if is_fixed(con.body):
if self._skip_trivial_constraints:
return None
conname = self._symbol_map.getSymbol(con, self._labeler)
if con._linear_canonical_form:
gurobi_expr, referenced_vars = self._get_expr_from_pyomo_repn(
con.canonical_form(),
self._max_constraint_degree)
#elif isinstance(con, LinearCanonicalRepn):
# gurobi_expr, referenced_vars = self._get_expr_from_pyomo_repn(
# con,
# self._max_constraint_degree)
else:
gurobi_expr, referenced_vars = self._get_expr_from_pyomo_expr(
con.body,
self._max_constraint_degree)
if con.has_lb():
if not is_fixed(con.lower):
raise ValueError("Lower bound of constraint {0} "
"is not constant.".format(con))
if con.has_ub():
if not is_fixed(con.upper):
raise ValueError("Upper bound of constraint {0} "
"is not constant.".format(con))
if con.equality:
gurobipy_con = self._solver_model.addConstr(lhs=gurobi_expr,
sense=self._gurobipy.GRB.EQUAL,
rhs=value(con.lower),
name=conname)
elif con.has_lb() and con.has_ub():
gurobipy_con = self._solver_model.addRange(gurobi_expr,
value(con.lower),
value(con.upper),
name=conname)
self._range_constraints.add(con)
elif con.has_lb():
gurobipy_con = self._solver_model.addConstr(lhs=gurobi_expr,
sense=self._gurobipy.GRB.GREATER_EQUAL,
rhs=value(con.lower),
name=conname)
elif con.has_ub():
gurobipy_con = self._solver_model.addConstr(lhs=gurobi_expr,
sense=self._gurobipy.GRB.LESS_EQUAL,
rhs=value(con.upper),
name=conname)
else:
raise ValueError("Constraint does not have a lower "
"or an upper bound: {0} \n".format(con))
for var in referenced_vars:
self._referenced_variables[var] += 1
self._vars_referenced_by_con[con] = referenced_vars
self._pyomo_con_to_solver_con_map[con] = gurobipy_con
self._solver_con_to_pyomo_con_map[gurobipy_con] = con
def _add_sos_constraint(self, con):
if not con.active:
return None
conname = self._symbol_map.getSymbol(con, self._labeler)
level = con.level
if level == 1:
sos_type = self._gurobipy.GRB.SOS_TYPE1
elif level == 2:
sos_type = self._gurobipy.GRB.SOS_TYPE2
else:
raise ValueError("Solver does not support SOS "
"level {0} constraints".format(level))
gurobi_vars = []
weights = []
self._vars_referenced_by_con[con] = ComponentSet()
if hasattr(con, 'get_items'):
# aml sos constraint
sos_items = list(con.get_items())
else:
# kernel sos constraint
sos_items = list(con.items())
for v, w in sos_items:
self._vars_referenced_by_con[con].add(v)
gurobi_vars.append(self._pyomo_var_to_solver_var_map[v])
self._referenced_variables[v] += 1
weights.append(w)
gurobipy_con = self._solver_model.addSOS(sos_type, gurobi_vars, weights)
self._pyomo_con_to_solver_con_map[con] = gurobipy_con
self._solver_con_to_pyomo_con_map[gurobipy_con] = con
def _gurobi_vtype_from_var(self, var):
"""
This function takes a pyomo variable and returns the appropriate gurobi variable type
:param var: pyomo.core.base.var.Var
:return: gurobipy.GRB.CONTINUOUS or gurobipy.GRB.BINARY or gurobipy.GRB.INTEGER
"""
if var.is_binary():
vtype = self._gurobipy.GRB.BINARY
elif var.is_integer():
vtype = self._gurobipy.GRB.INTEGER
elif var.is_continuous():
vtype = self._gurobipy.GRB.CONTINUOUS
else:
raise ValueError('Variable domain type is not recognized for {0}'.format(var.domain))
return vtype
def _set_objective(self, obj):
if self._objective is not None:
for var in self._vars_referenced_by_obj:
self._referenced_variables[var] -= 1
self._vars_referenced_by_obj = ComponentSet()
self._objective = None
if obj.active is False:
raise ValueError('Cannot add inactive objective to solver.')
if obj.sense == minimize:
sense = self._gurobipy.GRB.MINIMIZE
elif obj.sense == maximize:
sense = self._gurobipy.GRB.MAXIMIZE
else:
raise ValueError('Objective sense is not recognized: {0}'.format(obj.sense))
gurobi_expr, referenced_vars = self._get_expr_from_pyomo_expr(obj.expr, self._max_obj_degree)
for var in referenced_vars:
self._referenced_variables[var] += 1
self._solver_model.setObjective(gurobi_expr, sense=sense)
self._objective = obj
self._vars_referenced_by_obj = referenced_vars
def _postsolve(self):
# the only suffixes that we extract from GUROBI are
# constraint duals, constraint slacks, and variable
# reduced-costs. scan through the solver suffix list
# and throw an exception if the user has specified
# any others.
extract_duals = False
extract_slacks = False
extract_reduced_costs = False
for suffix in self._suffixes:
flag = False
if re.match(suffix, "dual"):
extract_duals = True
flag = True
if re.match(suffix, "slack"):
extract_slacks = True
flag = True
if re.match(suffix, "rc"):
extract_reduced_costs = True
flag = True
if not flag:
raise RuntimeError("***The gurobi_direct solver plugin cannot extract solution suffix="+suffix)
gprob = self._solver_model
grb = self._gurobipy.GRB
status = gprob.Status
if gprob.getAttr(self._gurobipy.GRB.Attr.IsMIP):
if extract_reduced_costs:
logger.warning("Cannot get reduced costs for MIP.")
if extract_duals:
logger.warning("Cannot get duals for MIP.")
extract_reduced_costs = False
extract_duals = False
self.results = SolverResults()
soln = Solution()
self.results.solver.name = self._name
self.results.solver.wallclock_time = gprob.Runtime
if status == grb.LOADED: # problem is loaded, but no solution
self.results.solver.status = SolverStatus.aborted
self.results.solver.termination_message = "Model is loaded, but no solution information is available."
self.results.solver.termination_condition = TerminationCondition.error
soln.status = SolutionStatus.unknown
elif status == grb.OPTIMAL: # optimal
self.results.solver.status = SolverStatus.ok
self.results.solver.termination_message = "Model was solved to optimality (subject to tolerances), " \
"and an optimal solution is available."
self.results.solver.termination_condition = TerminationCondition.optimal
soln.status = SolutionStatus.optimal
elif status == grb.INFEASIBLE:
self.results.solver.status = SolverStatus.warning
self.results.solver.termination_message = "Model was proven to be infeasible"
self.results.solver.termination_condition = TerminationCondition.infeasible
soln.status = SolutionStatus.infeasible
elif status == grb.INF_OR_UNBD:
self.results.solver.status = SolverStatus.warning
self.results.solver.termination_message = "Problem proven to be infeasible or unbounded."
self.results.solver.termination_condition = TerminationCondition.infeasibleOrUnbounded
soln.status = SolutionStatus.unsure
elif status == grb.UNBOUNDED:
self.results.solver.status = SolverStatus.warning
self.results.solver.termination_message = "Model was proven to be unbounded."
self.results.solver.termination_condition = TerminationCondition.unbounded
soln.status = SolutionStatus.unbounded
elif status == grb.CUTOFF:
self.results.solver.status = SolverStatus.aborted
self.results.solver.termination_message = "Optimal objective for model was proven to be worse than the " \
"value specified in the Cutoff parameter. No solution " \
"information is available."
self.results.solver.termination_condition = TerminationCondition.minFunctionValue
soln.status = SolutionStatus.unknown
elif status == grb.ITERATION_LIMIT:
self.results.solver.status = SolverStatus.aborted
self.results.solver.termination_message = "Optimization terminated because the total number of simplex " \
"iterations performed exceeded the value specified in the " \
"IterationLimit parameter."
self.results.solver.termination_condition = TerminationCondition.maxIterations
soln.status = SolutionStatus.stoppedByLimit
elif status == grb.NODE_LIMIT:
self.results.solver.status = SolverStatus.aborted
self.results.solver.termination_message = "Optimization terminated because the total number of " \
"branch-and-cut nodes explored exceeded the value specified " \
"in the NodeLimit parameter"
self.results.solver.termination_condition = TerminationCondition.maxEvaluations
soln.status = SolutionStatus.stoppedByLimit
elif status == grb.TIME_LIMIT:
self.results.solver.status = SolverStatus.aborted
self.results.solver.termination_message = "Optimization terminated because the time expended exceeded " \
"the value specified in the TimeLimit parameter."
self.results.solver.termination_condition = TerminationCondition.maxTimeLimit
soln.status = SolutionStatus.stoppedByLimit
elif status == grb.SOLUTION_LIMIT:
self.results.solver.status = SolverStatus.aborted
self.results.solver.termination_message = "Optimization terminated because the number of solutions found " \
"reached the value specified in the SolutionLimit parameter."
self.results.solver.termination_condition = TerminationCondition.unknown
soln.status = SolutionStatus.stoppedByLimit
elif status == grb.INTERRUPTED:
self.results.solver.status = SolverStatus.aborted
self.results.solver.termination_message = "Optimization was terminated by the user."
self.results.solver.termination_condition = TerminationCondition.error
soln.status = SolutionStatus.error
elif status == grb.NUMERIC:
self.results.solver.status = SolverStatus.error
self.results.solver.termination_message = "Optimization was terminated due to unrecoverable numerical " \
"difficulties."
self.results.solver.termination_condition = TerminationCondition.error
soln.status = SolutionStatus.error
elif status == grb.SUBOPTIMAL:
self.results.solver.status = SolverStatus.warning
self.results.solver.termination_message = "Unable to satisfy optimality tolerances; a sub-optimal " \
"solution is available."
self.results.solver.termination_condition = TerminationCondition.other
soln.status = SolutionStatus.feasible
# note that USER_OBJ_LIMIT was added in Gurobi 7.0, so it may not be present
elif (status is not None) and \
(status == getattr(grb,'USER_OBJ_LIMIT',None)):
self.results.solver.status = SolverStatus.aborted
self.results.solver.termination_message = "User specified an objective limit " \
"(a bound on either the best objective " \
"or the best bound), and that limit has " \
"been reached. Solution is available."
self.results.solver.termination_condition = TerminationCondition.other
soln.status = SolutionStatus.stoppedByLimit
else:
self.results.solver.status = SolverStatus.error
self.results.solver.termination_message = \
("Unhandled Gurobi solve status "
"("+str(status)+")")
self.results.solver.termination_condition = TerminationCondition.error
soln.status = SolutionStatus.error
self.results.problem.name = gprob.ModelName
if gprob.ModelSense == 1:
self.results.problem.sense = minimize
elif gprob.ModelSense == -1:
self.results.problem.sense = maximize
else:
raise RuntimeError('Unrecognized gurobi objective sense: {0}'.format(gprob.ModelSense))
self.results.problem.upper_bound = None
self.results.problem.lower_bound = None
if (gprob.NumBinVars + gprob.NumIntVars) == 0:
try:
self.results.problem.upper_bound = gprob.ObjVal
self.results.problem.lower_bound = gprob.ObjVal
except (self._gurobipy.GurobiError, AttributeError):
pass
elif gprob.ModelSense == 1: # minimizing
try:
self.results.problem.upper_bound = gprob.ObjVal
except (self._gurobipy.GurobiError, AttributeError):
pass
try:
self.results.problem.lower_bound = gprob.ObjBound
except (self._gurobipy.GurobiError, AttributeError):
pass
elif gprob.ModelSense == -1: # maximizing
try:
self.results.problem.upper_bound = gprob.ObjBound
except (self._gurobipy.GurobiError, AttributeError):
pass
try:
self.results.problem.lower_bound = gprob.ObjVal
except (self._gurobipy.GurobiError, AttributeError):
pass
else:
raise RuntimeError('Unrecognized gurobi objective sense: {0}'.format(gprob.ModelSense))
try:
soln.gap = self.results.problem.upper_bound - self.results.problem.lower_bound
except TypeError:
soln.gap = None
self.results.problem.number_of_constraints = gprob.NumConstrs + gprob.NumQConstrs + gprob.NumSOS
self.results.problem.number_of_nonzeros = gprob.NumNZs
self.results.problem.number_of_variables = gprob.NumVars
self.results.problem.number_of_binary_variables = gprob.NumBinVars
self.results.problem.number_of_integer_variables = gprob.NumIntVars
self.results.problem.number_of_continuous_variables = gprob.NumVars - gprob.NumIntVars - gprob.NumBinVars
self.results.problem.number_of_objectives = 1
self.results.problem.number_of_solutions = gprob.SolCount
# if a solve was stopped by a limit, we still need to check to
# see if there is a solution available - this may not always
# be the case, both in LP and MIP contexts.
if self._save_results:
"""
This code in this if statement is only needed for backwards compatability. It is more efficient to set
_save_results to False and use load_vars, load_duals, etc.
"""
if gprob.SolCount > 0:
soln_variables = soln.variable
soln_constraints = soln.constraint
gurobi_vars = self._solver_model.getVars()
gurobi_vars = list(set(gurobi_vars).intersection(set(self._pyomo_var_to_solver_var_map.values())))
var_vals = self._solver_model.getAttr("X", gurobi_vars)
names = self._solver_model.getAttr("VarName", gurobi_vars)
for gurobi_var, val, name in zip(gurobi_vars, var_vals, names):
pyomo_var = self._solver_var_to_pyomo_var_map[gurobi_var]
if self._referenced_variables[pyomo_var] > 0:
pyomo_var.stale = False
soln_variables[name] = {"Value": val}
if extract_reduced_costs:
vals = self._solver_model.getAttr("Rc", gurobi_vars)
for gurobi_var, val, name in zip(gurobi_vars, vals, names):
pyomo_var = self._solver_var_to_pyomo_var_map[gurobi_var]
if self._referenced_variables[pyomo_var] > 0:
soln_variables[name]["Rc"] = val
if extract_duals or extract_slacks:
gurobi_cons = self._solver_model.getConstrs()
con_names = self._solver_model.getAttr("ConstrName", gurobi_cons)
for name in con_names:
soln_constraints[name] = {}
if self._version_major >= 5:
gurobi_q_cons = self._solver_model.getQConstrs()
q_con_names = self._solver_model.getAttr("QCName", gurobi_q_cons)
for name in q_con_names:
soln_constraints[name] = {}
if extract_duals:
vals = self._solver_model.getAttr("Pi", gurobi_cons)
for val, name in zip(vals, con_names):
soln_constraints[name]["Dual"] = val
if self._version_major >= 5:
q_vals = self._solver_model.getAttr("QCPi", gurobi_q_cons)
for val, name in zip(q_vals, q_con_names):
soln_constraints[name]["Dual"] = val
if extract_slacks:
gurobi_range_con_vars = set(self._solver_model.getVars()) - set(self._pyomo_var_to_solver_var_map.values())
vals = self._solver_model.getAttr("Slack", gurobi_cons)
for gurobi_con, val, name in zip(gurobi_cons, vals, con_names):
pyomo_con = self._solver_con_to_pyomo_con_map[gurobi_con]
if pyomo_con in self._range_constraints:
lin_expr = self._solver_model.getRow(gurobi_con)
for i in reversed(range(lin_expr.size())):
v = lin_expr.getVar(i)
if v in gurobi_range_con_vars:
Us_ = v.X
Ls_ = v.UB - v.X
if Us_ > Ls_:
soln_constraints[name]["Slack"] = Us_
else:
soln_constraints[name]["Slack"] = -Ls_
break
else:
soln_constraints[name]["Slack"] = val
if self._version_major >= 5:
q_vals = self._solver_model.getAttr("QCSlack", gurobi_q_cons)
for val, name in zip(q_vals, q_con_names):
soln_constraints[name]["Slack"] = val
elif self._load_solutions:
if gprob.SolCount > 0:
self._load_vars()
if extract_reduced_costs:
self._load_rc()
if extract_duals:
self._load_duals()
if extract_slacks:
self._load_slacks()
self.results.solution.insert(soln)
# finally, clean any temporary files registered with the temp file
# manager, created populated *directly* by this plugin.
TempfileManager.pop(remove=not self._keepfiles)
return DirectOrPersistentSolver._postsolve(self)
def warm_start_capable(self):
return True
def _warm_start(self):
for pyomo_var, gurobipy_var in self._pyomo_var_to_solver_var_map.items():
if pyomo_var.value is not None:
gurobipy_var.setAttr(self._gurobipy.GRB.Attr.Start, value(pyomo_var))
def _load_vars(self, vars_to_load=None):
var_map = self._pyomo_var_to_solver_var_map
ref_vars = self._referenced_variables
if vars_to_load is None:
vars_to_load = var_map.keys()
gurobi_vars_to_load = [var_map[pyomo_var] for pyomo_var in vars_to_load]
vals = self._solver_model.getAttr("X", gurobi_vars_to_load)
for var, val in zip(vars_to_load, vals):
if ref_vars[var] > 0:
var.stale = False
var.value = val
def _load_rc(self, vars_to_load=None):
if not hasattr(self._pyomo_model, 'rc'):
self._pyomo_model.rc = Suffix(direction=Suffix.IMPORT)
var_map = self._pyomo_var_to_solver_var_map
ref_vars = self._referenced_variables
rc = self._pyomo_model.rc
if vars_to_load is None:
vars_to_load = var_map.keys()
gurobi_vars_to_load = [var_map[pyomo_var] for pyomo_var in vars_to_load]
vals = self._solver_model.getAttr("Rc", gurobi_vars_to_load)
for var, val in zip(vars_to_load, vals):
if ref_vars[var] > 0:
rc[var] = val
def _load_duals(self, cons_to_load=None):
if not hasattr(self._pyomo_model, 'dual'):
self._pyomo_model.dual = Suffix(direction=Suffix.IMPORT)
con_map = self._pyomo_con_to_solver_con_map
reverse_con_map = self._solver_con_to_pyomo_con_map
dual = self._pyomo_model.dual
if cons_to_load is None:
linear_cons_to_load = self._solver_model.getConstrs()
if self._version_major >= 5:
quadratic_cons_to_load = self._solver_model.getQConstrs()
else:
gurobi_cons_to_load = set([con_map[pyomo_con] for pyomo_con in cons_to_load])
linear_cons_to_load = gurobi_cons_to_load.intersection(set(self._solver_model.getConstrs()))
if self._version_major >= 5:
quadratic_cons_to_load = gurobi_cons_to_load.intersection(set(self._solver_model.getQConstrs()))
linear_vals = self._solver_model.getAttr("Pi", linear_cons_to_load)
if self._version_major >= 5:
quadratic_vals = self._solver_model.getAttr("QCPi", quadratic_cons_to_load)
for gurobi_con, val in zip(linear_cons_to_load, linear_vals):
pyomo_con = reverse_con_map[gurobi_con]
dual[pyomo_con] = val
if self._version_major >= 5:
for gurobi_con, val in zip(quadratic_cons_to_load, quadratic_vals):
pyomo_con = reverse_con_map[gurobi_con]
dual[pyomo_con] = val
def _load_slacks(self, cons_to_load=None):
if not hasattr(self._pyomo_model, 'slack'):
self._pyomo_model.slack = Suffix(direction=Suffix.IMPORT)
con_map = self._pyomo_con_to_solver_con_map
reverse_con_map = self._solver_con_to_pyomo_con_map
slack = self._pyomo_model.slack
gurobi_range_con_vars = set(self._solver_model.getVars()) - set(self._pyomo_var_to_solver_var_map.values())
if cons_to_load is None:
linear_cons_to_load = self._solver_model.getConstrs()
if self._version_major >= 5:
quadratic_cons_to_load = self._solver_model.getQConstrs()
else:
gurobi_cons_to_load = set([con_map[pyomo_con] for pyomo_con in cons_to_load])
linear_cons_to_load = gurobi_cons_to_load.intersection(set(self._solver_model.getConstrs()))
if self._version_major >= 5:
quadratic_cons_to_load = gurobi_cons_to_load.intersection(set(self._solver_model.getQConstrs()))
linear_vals = self._solver_model.getAttr("Slack", linear_cons_to_load)
if self._version_major >= 5:
quadratic_vals = self._solver_model.getAttr("QCSlack", quadratic_cons_to_load)
for gurobi_con, val in zip(linear_cons_to_load, linear_vals):
pyomo_con = reverse_con_map[gurobi_con]
if pyomo_con in self._range_constraints:
lin_expr = self._solver_model.getRow(gurobi_con)
for i in reversed(range(lin_expr.size())):
v = lin_expr.getVar(i)
if v in gurobi_range_con_vars:
Us_ = v.X
Ls_ = v.UB - v.X
if Us_ > Ls_:
slack[pyomo_con] = Us_
else:
slack[pyomo_con] = -Ls_
break
else:
slack[pyomo_con] = val
if self._version_major >= 5:
for gurobi_con, val in zip(quadratic_cons_to_load, quadratic_vals):
pyomo_con = reverse_con_map[gurobi_con]
slack[pyomo_con] = val
def load_duals(self, cons_to_load=None):
"""
Load the duals into the 'dual' suffix. The 'dual' suffix must live on the parent model.
Parameters
----------
cons_to_load: list of Constraint
"""
self._load_duals(cons_to_load)
def load_rc(self, vars_to_load):
"""
Load the reduced costs into the 'rc' suffix. The 'rc' suffix must live on the parent model.
Parameters
----------
vars_to_load: list of Var
"""
self._load_rc(vars_to_load)
def load_slacks(self, cons_to_load=None):
"""
Load the values of the slack variables into the 'slack' suffix. The 'slack' suffix must live on the parent
model.
Parameters
----------
cons_to_load: list of Constraint
"""
self._load_slacks(cons_to_load)
class _PortData(ComponentData):
"""
This class defines the data for a single Port
Attributes
----------
vars:`dict`
A dictionary mapping added names to variables
"""
__slots__ = ('vars', '_arcs', '_sources', '_dests', '_rules', '_splitfracs')
def __init__(self, component=None):
#
# These lines represent in-lining of the
# following constructors:
# - ComponentData
# - NumericValue
self._component = weakref_ref(component) if (component is not None) \
else None
self.vars = {}
self._arcs = []
self._sources = []
self._dests = []
self._rules = {}
self._splitfracs = ComponentMap()
def __getstate__(self):
state = super(_PortData, self).__getstate__()
for i in _PortData.__slots__:
state[i] = getattr(self, i)
return state
# Note: None of the slots on this class need to be edited, so we
# don't need to implement a specialized __setstate__ method, and
# can quietly rely on the super() class's implementation.
def __getattr__(self, name):
"""Returns `self.vars[name]` if it exists"""
if name in self.vars:
return self.vars[name]
# Since the base classes don't support getattr, we can just
# throw the "normal" AttributeError
raise AttributeError("'%s' object has no attribute '%s'"
% (self.__class__.__name__, name))
def arcs(self, active=None):
"""A list of Arcs in which this Port is a member"""
return self._collect_ports(active, self._arcs)
def sources(self, active=None):
"""A list of Arcs in which this Port is a destination"""
return self._collect_ports(active, self._sources)
def dests(self, active=None):
"""A list of Arcs in which this Port is a source"""
return self._collect_ports(active, self._dests)
def _collect_ports(self, active, port_list):
# need to call the weakrefs
if active is None:
return [_a() for _a in port_list]
tmp = []
for _a in port_list:
a = _a()
if a.active == active:
tmp.append(a)
return tmp
def set_value(self, value):
"""Cannot specify the value of a port"""
raise ValueError("Cannot specify the value of a port: '%s'" % self.name)
def polynomial_degree(self):
"""Returns the maximum polynomial degree of all port members"""
ans = 0
for v in self.iter_vars():
tmp = v.polynomial_degree()
if tmp is None:
return None
ans = max(ans, tmp)
return ans
def is_fixed(self):
"""Return True if all vars/expressions in the Port are fixed"""
return all(v.is_fixed() for v in self.iter_vars())
def is_potentially_variable(self):
"""Return True as ports may (should!) contain variables"""
return True
def is_binary(self):
"""Return True if all variables in the Port are binary"""
return len(self) and all(
v.is_binary() for v in self.iter_vars(expr_vars=True))
def is_integer(self):
"""Return True if all variables in the Port are integer"""
return len(self) and all(
v.is_integer() for v in self.iter_vars(expr_vars=True))
def is_continuous(self):
"""Return True if all variables in the Port are continuous"""
return len(self) and all(
v.is_continuous() for v in self.iter_vars(expr_vars=True))
def add(self, var, name=None, rule=None, **kwds):
"""
Add `var` to this Port, casting it to a Pyomo numeric if necessary
Arguments
---------
var
A variable or some `NumericValue` like an expression
name: `str`
Name to associate with this member of the Port
rule: `function`
Function implementing the desired expansion procedure
for this member. `Port.Equality` by default, other
options include `Port.Extensive`. Customs are allowed.
kwds
Keyword arguments that will be passed to rule
"""
if var is not None:
try:
# indexed components are ok, but as_numeric will error on them
# make sure they have this attribute
var.is_indexed()
except AttributeError:
var = as_numeric(var)
if name is None:
name = var.local_name
if name in self.vars and self.vars[name] is not None:
# don't throw warning if replacing an implicit (None) var
logger.warning("Implicitly replacing variable '%s' in Port '%s'.\n"
"To avoid this warning, use Port.remove() first."
% (name, self.name))
self.vars[name] = var
if rule is None:
rule = Port.Equality
if rule is Port.Extensive:
# avoid name collisions
if (name.endswith("_split") or name.endswith("_equality") or
name == "splitfrac"):
raise ValueError(
"Extensive variable '%s' on Port '%s' may not end "
"with '_split' or '_equality'" % (name, self.name))
self._rules[name] = (rule, kwds)
def remove(self, name):
"""Remove this member from the port"""
if name not in self.vars:
raise ValueError("Cannot remove member '%s' not in Port '%s'"
% (name, self.name))
self.vars.pop(name)
self._rules.pop(name)
def rule_for(self, name):
"""Return the rule associated with the given port member"""
return self._rules[name][0]
def is_equality(self, name):
"""Return True if the rule for this port member is Port.Equality"""
return self.rule_for(name) is Port.Equality
def is_extensive(self, name):
"""Return True if the rule for this port member is Port.Extensive"""
return self.rule_for(name) is Port.Extensive
def fix(self):
"""
Fix all variables in the port at their current values.
For expressions, fix every variable in the expression.
"""
for v in self.iter_vars(expr_vars=True, fixed=False):
v.fix()
def unfix(self):
"""
Unfix all variables in the port.
For expressions, unfix every variable in the expression.
"""
for v in self.iter_vars(expr_vars=True, fixed=True):
v.unfix()
free = unfix
def iter_vars(self, expr_vars=False, fixed=None, names=False):
"""
Iterate through every member of the port, going through
the indices of indexed members.
Arguments
---------
expr_vars: `bool`
If True, call `identify_variables` on expression type members
fixed: `bool`
Only include variables/expressions with this type of fixed
names: `bool`
If True, yield (name, var/expr) pairs
"""
for name, mem in iteritems(self.vars):
if not mem.is_indexed():
itr = (mem,)
else:
itr = itervalues(mem)
for v in itr:
if fixed is not None and v.is_fixed() != fixed:
continue
if expr_vars and v.is_expression_type():
for var in identify_variables(v):
if fixed is not None and var.is_fixed() != fixed:
continue
if names:
yield name, var
else:
yield var
else:
if names:
yield name, v
else:
yield v
def set_split_fraction(self, arc, val, fix=True):
"""
Set the split fraction value to be used for an arc during
arc expansion when using `Port.Extensive`.
"""
if arc not in self.dests():
raise ValueError("Port '%s' is not a source of Arc '%s', cannot "
"set split fraction" % (self.name, arc.name))
self._splitfracs[arc] = (val, fix)
def get_split_fraction(self, arc):
"""
Returns a tuple (val, fix) for the split fraction of this arc that
was set via `set_split_fraction` if it exists, and otherwise None.
"""
res = self._splitfracs.get(arc, None)
if res is None:
return None
else:
return res
def test_keys(self):
cmap = ComponentMap(self._components)
self.assertEqual(sorted(cmap.keys(), key=id),
sorted(list(c for c,val in self._components),
key=id))
def fbbt_block(m, tol=1e-4, deactivate_satisfied_constraints=False, integer_tol=1e-5, infeasible_tol=1e-8):
"""
Feasibility based bounds tightening (FBBT) for a block or model. This
loops through all of the constraints in the block and performs
FBBT on each constraint (see the docstring for fbbt_con()).
Through this processes, any variables whose bounds improve
by more than tol are collected, and FBBT is
performed again on all constraints involving those variables.
This process is continued until no variable bounds are improved
by more than tol.
Parameters
----------
m: pyomo.core.base.block.Block or pyomo.core.base.PyomoModel.ConcreteModel
tol: float
deactivate_satisfied_constraints: bool
If deactivate_satisfied_constraints is True and a constraint is always satisfied, then the constranit
will be deactivated
integer_tol: float
If the lower bound computed on a binary variable is less than or equal to integer_tol, then the
lower bound is left at 0. Otherwise, the lower bound is increased to 1. If the upper bound computed
on a binary variable is greater than or equal to 1-integer_tol, then the upper bound is left at 1.
Otherwise the upper bound is decreased to 0.
infeasible_tol: float
If the bounds computed on the body of a constraint violate the bounds of the constraint by more than
infeasible_tol, then the constraint is considered infeasible and an exception is raised.
Returns
-------
new_var_bounds: ComponentMap
A ComponentMap mapping from variables a tuple containing the lower and upper bounds, respectively, computed
from FBBT.
"""
new_var_bounds = ComponentMap()
var_to_con_map = ComponentMap()
var_lbs = ComponentMap()
var_ubs = ComponentMap()
for c in m.component_data_objects(ctype=Constraint, active=True,
descend_into=True, sort=True):
for v in identify_variables(c.body):
if v not in var_to_con_map:
var_to_con_map[v] = list()
if v.lb is None:
var_lbs[v] = -math.inf
else:
var_lbs[v] = value(v.lb)
if v.ub is None:
var_ubs[v] = math.inf
else:
var_ubs[v] = value(v.ub)
var_to_con_map[v].append(c)
for _v in m.component_data_objects(ctype=Var, active=True, descend_into=True, sort=True):
if _v.is_fixed():
_v.setlb(_v.value)
_v.setub(_v.value)
new_var_bounds[_v] = (_v.value, _v.value)
improved_vars = ComponentSet()
for c in m.component_data_objects(ctype=Constraint, active=True,
descend_into=True, sort=True):
_new_var_bounds = fbbt_con(c, deactivate_satisfied_constraints=deactivate_satisfied_constraints,
integer_tol=integer_tol, infeasible_tol=infeasible_tol)
new_var_bounds.update(_new_var_bounds)
for v, bnds in _new_var_bounds.items():
vlb, vub = bnds
if vlb is not None:
if vlb > var_lbs[v] + tol:
improved_vars.add(v)
var_lbs[v] = vlb
if vub is not None:
if vub < var_ubs[v] - tol:
improved_vars.add(v)
var_ubs[v] = vub
while len(improved_vars) > 0:
v = improved_vars.pop()
for c in var_to_con_map[v]:
_new_var_bounds = fbbt_con(c, deactivate_satisfied_constraints=deactivate_satisfied_constraints,
integer_tol=integer_tol, infeasible_tol=infeasible_tol)
new_var_bounds.update(_new_var_bounds)
for _v, bnds in _new_var_bounds.items():
_vlb, _vub = bnds
if _vlb is not None:
if _vlb > var_lbs[_v] + tol:
improved_vars.add(_v)
var_lbs[_v] = _vlb
if _vub is not None:
if _vub < var_ubs[_v] - tol:
improved_vars.add(_v)
var_ubs[_v] = _vub
return new_var_bounds
def fbbt_con(con, deactivate_satisfied_constraints=False, integer_tol=1e-5, infeasible_tol=1e-8):
"""
Feasibility based bounds tightening for a constraint. This function attempts to improve the bounds of each variable
in the constraint based on the bounds of the constraint and the bounds of the other variables in the constraint.
For example:
>>> import pyomo.environ as pe
>>> from pyomo.contrib.fbbt.fbbt import fbbt
>>> m = pe.ConcreteModel()
>>> m.x = pe.Var(bounds=(-1,1))
>>> m.y = pe.Var(bounds=(-2,2))
>>> m.z = pe.Var()
>>> m.c = pe.Constraint(expr=m.x*m.y + m.z == 1)
>>> fbbt(m.c)
>>> print(m.z.lb, m.z.ub)
-1.0 3.0
Parameters
----------
con: pyomo.core.base.constraint.Constraint
constraint on which to perform fbbt
deactivate_satisfied_constraints: bool
If deactivate_satisfied_constraints is True and the constraint is always satisfied, then the constranit
will be deactivated
integer_tol: float
If the lower bound computed on a binary variable is less than or equal to integer_tol, then the
lower bound is left at 0. Otherwise, the lower bound is increased to 1. If the upper bound computed
on a binary variable is greater than or equal to 1-integer_tol, then the upper bound is left at 1.
Otherwise the upper bound is decreased to 0.
infeasible_tol: float
If the bounds computed on the body of a constraint violate the bounds of the constraint by more than
infeasible_tol, then the constraint is considered infeasible and an exception is raised.
Returns
-------
new_var_bounds: ComponentMap
A ComponentMap mapping from variables a tuple containing the lower and upper bounds, respectively, computed
from FBBT.
"""
if not con.active:
return ComponentMap()
bnds_dict = ComponentMap() # a dictionary to store the bounds of every node in the tree
# a walker to propagate bounds from the variables to the root
visitorA = _FBBTVisitorLeafToRoot(bnds_dict)
visitorA.dfs_postorder_stack(con.body)
# Now we need to replace the bounds in bnds_dict for the root
# node with the bounds on the constraint (if those bounds are
# better).
_lb = value(con.lower)
_ub = value(con.upper)
if _lb is None:
_lb = -math.inf
if _ub is None:
_ub = math.inf
lb, ub = bnds_dict[con.body]
# check if the constraint is infeasible
if lb > _ub + infeasible_tol or ub < _lb - infeasible_tol:
raise InfeasibleConstraintException('Detected an infeasible constraint during FBBT: {0}'.format(str(con)))
# check if the constraint is always satisfied
if deactivate_satisfied_constraints:
if lb >= _lb and ub <= _ub:
con.deactivate()
if _lb > lb:
lb = _lb
if _ub < ub:
ub = _ub
bnds_dict[con.body] = (lb, ub)
# Now, propagate bounds back from the root to the variables
visitorB = _FBBTVisitorRootToLeaf(bnds_dict, integer_tol=integer_tol)
visitorB.dfs_postorder_stack(con.body)
new_var_bounds = ComponentMap()
for _node, _bnds in bnds_dict.items():
if _node.__class__ in nonpyomo_leaf_types:
continue
if _node.is_variable_type():
lb, ub = bnds_dict[_node]
if lb == -math.inf:
lb = None
if ub == math.inf:
ub = None
new_var_bounds[_node] = (lb, ub)
return new_var_bounds
def test_str(self):
cmap = ComponentMap()
self.assertEqual(str(cmap), "ComponentMap({})")
cmap.update(self._components)
str(cmap)
def test_values(self):
cmap = ComponentMap(self._components)
self.assertEqual(sorted(cmap.values()),
sorted(list(val for c,val in self._components)))
