def _transform_constraintData(self, logical_constraint, new_varlists,
transBlocks):
# first find all the relevant BooleanVars and associate a binary (if
# they don't have one already)
for bool_vardata in identify_variables(logical_constraint.expr):
if bool_vardata.ctype is BooleanVar:
self._transform_boolean_varData(bool_vardata, new_varlists)
# now create a transformation block on the constraint's parent block (if
# we don't have one already)
parent_block = logical_constraint.parent_block()
xfrm_block = transBlocks.get(parent_block)
if xfrm_block is None:
xfrm_block = self._create_transformation_block(parent_block)
transBlocks[parent_block] = xfrm_block
new_constrlist = xfrm_block.transformed_constraints
new_boolvarlist = xfrm_block.augmented_vars
new_varlist = xfrm_block.augmented_vars_asbinary
old_boolvarlist_length = len(new_boolvarlist)
indicator_map = ComponentMap()
cnf_statements = to_cnf(logical_constraint.body, new_boolvarlist,
indicator_map)
logical_constraint.deactivate()
# Associate new Boolean vars to new binary variables
num_new = len(new_boolvarlist) - old_boolvarlist_length
list_o_vars = list(new_boolvarlist.values())
if num_new:
for bool_vardata in list_o_vars[-num_new:]:
new_binary_vardata = new_varlist.add()
bool_vardata.associate_binary_var(new_binary_vardata)
# Add constraints associated with each CNF statement
for cnf_statement in cnf_statements:
for linear_constraint in _cnf_to_linear_constraint_list(
cnf_statement):
new_constrlist.add(expr=linear_constraint)
# Add bigM associated with special atoms
# Note: this ad-hoc reformulation may be revisited for tightness in the
# future.
old_varlist_length = len(new_varlist)
for indicator_var, special_atom in indicator_map.items():
for linear_constraint in _cnf_to_linear_constraint_list(
special_atom,
indicator_var,
new_varlist):
new_constrlist.add(expr=linear_constraint)
# Previous step may have added auxiliary binaries. Associate augmented
# Booleans to them.
num_new = len(new_varlist) - old_varlist_length
list_o_vars = list(new_varlist.values())
if num_new:
for binary_vardata in list_o_vars[-num_new:]:
new_bool_vardata = new_boolvarlist.add()
new_bool_vardata.associate_binary_var(binary_vardata)
def test_cnf(self):
m = ConcreteModel()
m.Y1 = BooleanVar()
m.Y2 = BooleanVar()
implication = implies(m.Y1, m.Y2)
x = to_cnf(implication)[0]
_check_equivalent(self, implication, x)
atleast_expr = atleast(1, m.Y1, m.Y2)
x = to_cnf(atleast_expr)[0]
self.assertIs(atleast_expr, x) # should be no change
nestedatleast = implies(m.Y1, atleast_expr)
m.extraY = BooleanVarList()
indicator_map = ComponentMap()
x = to_cnf(nestedatleast, m.extraY, indicator_map)
self.assertEqual(str(x[0]), "extraY[1] ∨ ~Y1")
self.assertIs(indicator_map[m.extraY[1]], atleast_expr)
def _process_logical_constraints_in_logical_context(context):
new_xfrm_block_name = unique_component_name(context, 'logic_to_linear')
new_xfrm_block = Block(doc="Transformation objects for logic_to_linear")
setattr(context, new_xfrm_block_name, new_xfrm_block)
new_constrlist = new_xfrm_block.transformed_constraints = ConstraintList()
new_boolvarlist = new_xfrm_block.augmented_vars = BooleanVarList()
new_varlist = new_xfrm_block.augmented_vars_asbinary = VarList(
domain=Binary)
indicator_map = ComponentMap()
cnf_statements = []
# Convert all logical constraints to CNF
for logical_constraint in context.component_data_objects(
ctype=LogicalConstraint, active=True):
cnf_statements.extend(
to_cnf(logical_constraint.body, new_boolvarlist, indicator_map))
logical_constraint.deactivate()
# Associate new Boolean vars to new binary variables
for bool_vardata in new_boolvarlist.values():
new_binary_vardata = new_varlist.add()
bool_vardata.associate_binary_var(new_binary_vardata)
# Add constraints associated with each CNF statement
for cnf_statement in cnf_statements:
for linear_constraint in _cnf_to_linear_constraint_list(cnf_statement):
new_constrlist.add(expr=linear_constraint)
# Add bigM associated with special atoms
# Note: this ad-hoc reformulation may be revisited for tightness in the future.
old_varlist_length = len(new_varlist)
for indicator_var, special_atom in indicator_map.items():
for linear_constraint in _cnf_to_linear_constraint_list(
special_atom, indicator_var, new_varlist):
new_constrlist.add(expr=linear_constraint)
# Previous step may have added auxiliary binaries. Associate augmented Booleans to them.
num_new = len(new_varlist) - old_varlist_length
list_o_vars = list(new_varlist.values())
if num_new:
for binary_vardata in list_o_vars[-num_new:]:
new_bool_vardata = new_boolvarlist.add()
new_bool_vardata.associate_binary_var(binary_vardata)
# If added components were not used, remove them.
# Note: it is ok to simply delete the index_set for these components, because by
# default, a new set object is generated for each [Thing]List.
if len(new_constrlist) == 0:
new_xfrm_block.del_component(new_constrlist.index_set())
new_xfrm_block.del_component(new_constrlist)
if len(new_boolvarlist) == 0:
new_xfrm_block.del_component(new_boolvarlist.index_set())
new_xfrm_block.del_component(new_boolvarlist)
if len(new_varlist) == 0:
new_xfrm_block.del_component(new_varlist.index_set())
new_xfrm_block.del_component(new_varlist)
# If block was entirely unused, remove it
if all(
len(l) == 0
for l in (new_constrlist, new_boolvarlist, new_varlist)):
context.del_component(new_xfrm_block)