def _print_model_LP(self,
model,
output_file,
solver_capability,
labeler,
output_fixed_variable_bounds=False,
file_determinism=1,
row_order=None,
column_order=None,
skip_trivial_constraints=False,
force_objective_constant=False,
include_all_variable_bounds=False):
symbol_map = SymbolMap()
variable_symbol_map = SymbolMap()
# NOTE: we use createSymbol instead of getSymbol because we
# know whether or not the symbol exists, and don't want
# to the overhead of error/duplicate checking.
# cache frequently called functions
create_symbol_func = SymbolMap.createSymbol
create_symbols_func = SymbolMap.createSymbols
alias_symbol_func = SymbolMap.alias
variable_label_pairs = []
# populate the symbol map in a single pass.
#objective_list, constraint_list, sosconstraint_list, variable_list \
# = self._populate_symbol_map(model,
# symbol_map,
# labeler,
# variable_symbol_map,
# file_determinism=file_determinism)
sortOrder = SortComponents.unsorted
if file_determinism >= 1:
sortOrder = sortOrder | SortComponents.indices
if file_determinism >= 2:
sortOrder = sortOrder | SortComponents.alphabetical
#
# Create variable symbols (and cache the block list)
#
all_blocks = []
variable_list = []
for block in model.block_data_objects(active=True, sort=sortOrder):
all_blocks.append(block)
for vardata in block.component_data_objects(Var,
active=True,
sort=sortOrder,
descend_into=False):
variable_list.append(vardata)
variable_label_pairs.append(
(vardata, create_symbol_func(symbol_map, vardata,
labeler)))
#
# WEH - TODO: See if this is faster
#
#all_blocks = list( model.block_data_objects(
# active=True, sort=sortOrder) )
#variable_list = list( model.component_data_objects(
# Var, sort=sortOrder) )
#variable_label_pairs = list(
# (vardata, create_symbol_func(symbol_map, vardata, labeler))
# for vardata in variable_list )
variable_symbol_map.addSymbols(variable_label_pairs)
# and extract the information we'll need for rapid labeling.
object_symbol_dictionary = symbol_map.byObject
variable_symbol_dictionary = variable_symbol_map.byObject
# cache - these are called all the time.
print_expr_canonical = self._print_expr_canonical
# print the model name and the source, so we know roughly where
# it came from.
#
# NOTE: this *must* use the "\* ... *\" comment format: the GLPK
# LP parser does not correctly handle other formats (notably, "%").
output_file.write("\\* Source Pyomo model name=%s *\\\n\n" %
(model.name, ))
#
# Objective
#
supports_quadratic_objective = solver_capability('quadratic_objective')
numObj = 0
onames = []
for block in all_blocks:
gen_obj_repn = getattr(block, "_gen_obj_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block, '_repn'):
block._repn = ComponentMap()
block_repn = block._repn
for objective_data in block.component_data_objects(
Objective, active=True, sort=sortOrder,
descend_into=False):
numObj += 1
onames.append(objective_data.name)
if numObj > 1:
raise ValueError(
"More than one active objective defined for input "
"model '%s'; Cannot write legal LP file\n"
"Objectives: %s" % (model.name, ' '.join(onames)))
create_symbol_func(symbol_map, objective_data, labeler)
symbol_map.alias(objective_data, '__default_objective__')
if objective_data.is_minimizing():
output_file.write("min \n")
else:
output_file.write("max \n")
if gen_obj_repn:
repn = generate_standard_repn(objective_data.expr)
block_repn[objective_data] = repn
else:
repn = block_repn[objective_data]
degree = repn.polynomial_degree()
if degree == 0:
logger.warning(
"Constant objective detected, replacing "
"with a placeholder to prevent solver failure.")
force_objective_constant = True
elif degree == 2:
if not supports_quadratic_objective:
raise RuntimeError(
"Selected solver is unable to handle "
"objective functions with quadratic terms. "
"Objective at issue: %s." % objective_data.name)
elif degree is None:
raise RuntimeError(
"Cannot write legal LP file. Objective '%s' "
"has nonlinear terms that are not quadratic." %
objective_data.name)
output_file.write(
object_symbol_dictionary[id(objective_data)] + ':\n')
offset = print_expr_canonical(
repn,
output_file,
object_symbol_dictionary,
variable_symbol_dictionary,
True,
column_order,
force_objective_constant=force_objective_constant)
if numObj == 0:
raise ValueError("ERROR: No objectives defined for input model. "
"Cannot write legal LP file.")
# Constraints
#
# If there are no non-trivial constraints, you'll end up with an empty
# constraint block. CPLEX is OK with this, but GLPK isn't. And
# eliminating the constraint block (i.e., the "s.t." line) causes GLPK
# to whine elsewhere. Output a warning if the constraint block is empty,
# so users can quickly determine the cause of the solve failure.
output_file.write("\n")
output_file.write("s.t.\n")
output_file.write("\n")
have_nontrivial = False
supports_quadratic_constraint = solver_capability(
'quadratic_constraint')
def constraint_generator():
for block in all_blocks:
gen_con_repn = getattr(block, "_gen_con_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block, '_repn'):
block._repn = ComponentMap()
block_repn = block._repn
for constraint_data in block.component_data_objects(
Constraint,
active=True,
sort=sortOrder,
descend_into=False):
if (not constraint_data.has_lb()) and \
(not constraint_data.has_ub()):
assert not constraint_data.equality
continue # non-binding, so skip
if constraint_data._linear_canonical_form:
repn = constraint_data.canonical_form()
elif gen_con_repn:
repn = generate_standard_repn(constraint_data.body)
block_repn[constraint_data] = repn
else:
repn = block_repn[constraint_data]
yield constraint_data, repn
if row_order is not None:
sorted_constraint_list = list(constraint_generator())
sorted_constraint_list.sort(key=lambda x: row_order[x[0]])
def yield_all_constraints():
for data, repn in sorted_constraint_list:
yield data, repn
else:
yield_all_constraints = constraint_generator
# FIXME: This is a hack to get nested blocks working...
eq_string_template = "= %" + self._precision_string + '\n'
geq_string_template = ">= %" + self._precision_string + '\n\n'
leq_string_template = "<= %" + self._precision_string + '\n\n'
for constraint_data, repn in yield_all_constraints():
have_nontrivial = True
degree = repn.polynomial_degree()
#
# Write constraint
#
# There are conditions, e.g., when fixing variables, under which
# a constraint block might be empty. Ignore these, for both
# practical reasons and the fact that the CPLEX LP format
# requires a variable in the constraint body. It is also
# possible that the body of the constraint consists of only a
# constant, in which case the "variable" of
if degree == 0:
if skip_trivial_constraints:
continue
elif degree == 2:
if not supports_quadratic_constraint:
raise ValueError(
"Solver unable to handle quadratic expressions. Constraint"
" at issue: '%s'" % (constraint_data.name))
elif degree is None:
raise ValueError(
"Cannot write legal LP file. Constraint '%s' has a body "
"with nonlinear terms." % (constraint_data.name))
# Create symbol
con_symbol = create_symbol_func(symbol_map, constraint_data,
labeler)
if constraint_data.equality:
assert value(constraint_data.lower) == \
value(constraint_data.upper)
label = 'c_e_' + con_symbol + '_'
alias_symbol_func(symbol_map, constraint_data, label)
output_file.write(label + ':\n')
offset = print_expr_canonical(repn, output_file,
object_symbol_dictionary,
variable_symbol_dictionary,
False, column_order)
bound = constraint_data.lower
bound = _get_bound(bound) - offset
output_file.write(eq_string_template %
(_no_negative_zero(bound)))
output_file.write("\n")
else:
if constraint_data.has_lb():
if constraint_data.has_ub():
label = 'r_l_' + con_symbol + '_'
else:
label = 'c_l_' + con_symbol + '_'
alias_symbol_func(symbol_map, constraint_data, label)
output_file.write(label + ':\n')
offset = print_expr_canonical(repn, output_file,
object_symbol_dictionary,
variable_symbol_dictionary,
False, column_order)
bound = constraint_data.lower
bound = _get_bound(bound) - offset
output_file.write(geq_string_template %
(_no_negative_zero(bound)))
else:
assert constraint_data.has_ub()
if constraint_data.has_ub():
if constraint_data.has_lb():
label = 'r_u_' + con_symbol + '_'
else:
label = 'c_u_' + con_symbol + '_'
alias_symbol_func(symbol_map, constraint_data, label)
output_file.write(label + ':\n')
offset = print_expr_canonical(repn, output_file,
object_symbol_dictionary,
variable_symbol_dictionary,
False, column_order)
bound = constraint_data.upper
bound = _get_bound(bound) - offset
output_file.write(leq_string_template %
(_no_negative_zero(bound)))
else:
assert constraint_data.has_lb()
if not have_nontrivial:
logger.warning('Empty constraint block written in LP format ' \
'- solver may error')
# the CPLEX LP format doesn't allow constants in the objective (or
# constraint body), which is a bit silly. To avoid painful
# book-keeping, we introduce the following "variable", constrained
# to the value 1. This is used when quadratic terms are present.
# worst-case, if not used, is that CPLEX easily pre-processes it out.
prefix = ""
output_file.write('%sc_e_ONE_VAR_CONSTANT: \n' % prefix)
output_file.write('%sONE_VAR_CONSTANT = 1.0\n' % prefix)
output_file.write("\n")
# SOS constraints
#
# For now, we write out SOS1 and SOS2 constraints in the cplex format
#
# All Component objects are stored in model._component, which is a
# dictionary of {class: {objName: object}}.
#
# Consider the variable X,
#
# model.X = Var(...)
#
# We print X to CPLEX format as X(i,j,k,...) where i, j, k, ... are the
# indices of X.
#
SOSlines = StringIO()
sos1 = solver_capability("sos1")
sos2 = solver_capability("sos2")
writtenSOS = False
for block in all_blocks:
for soscondata in block.component_data_objects(SOSConstraint,
active=True,
sort=sortOrder,
descend_into=False):
create_symbol_func(symbol_map, soscondata, labeler)
level = soscondata.level
if (level == 1 and not sos1) or \
(level == 2 and not sos2) or \
(level > 2):
raise ValueError(
"Solver does not support SOS level %s constraints" %
(level))
if writtenSOS == False:
SOSlines.write("SOS\n")
writtenSOS = True
# This updates the referenced_variable_ids, just in case
# there is a variable that only appears in an
# SOSConstraint, in which case this needs to be known
# before we write the "bounds" section (Cplex does not
# handle this correctly, Gurobi does)
self.printSOS(symbol_map, labeler, variable_symbol_map,
soscondata, SOSlines)
#
# Bounds
#
output_file.write("bounds\n")
# Scan all variables even if we're only writing a subset of them.
# required because we don't store maps by variable type currently.
# FIXME: This is a hack to get nested blocks working...
lb_string_template = "%" + self._precision_string + " <= "
ub_string_template = " <= %" + self._precision_string + "\n"
# Track the number of integer and binary variables, so you can
# output their status later.
integer_vars = []
binary_vars = []
for vardata in variable_list:
# TODO: We could just loop over the set of items in
# self._referenced_variable_ids, except this is
# a dictionary that is hashed by id(vardata)
# which would make the bounds section
# nondeterministic (bad for unit testing)
if (not include_all_variable_bounds) and \
(id(vardata) not in self._referenced_variable_ids):
continue
if vardata.fixed:
if not 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 LP file." % (vardata.name, model.name))
if vardata.value is None:
raise ValueError(
"Variable cannot be fixed to a value of None.")
vardata_lb = value(vardata.value)
vardata_ub = value(vardata.value)
else:
vardata_lb = _get_bound(vardata.lb)
vardata_ub = _get_bound(vardata.ub)
name_to_output = variable_symbol_dictionary[id(vardata)]
# track the number of integer and binary variables, so we know whether
# to output the general / binary sections below.
if vardata.is_binary():
binary_vars.append(name_to_output)
elif vardata.is_integer():
integer_vars.append(name_to_output)
elif not vardata.is_continuous():
raise TypeError(
"Invalid domain type for variable with name '%s'. "
"Variable is not continuous, integer, or binary." %
(vardata.name))
# in the CPLEX LP file format, the default variable
# bounds are 0 and +inf. These bounds are in
# conflict with Pyomo, which assumes -inf and +inf
# (which we would argue is more rational).
output_file.write(" ")
if vardata.has_lb():
output_file.write(lb_string_template %
(_no_negative_zero(vardata_lb)))
else:
output_file.write(" -inf <= ")
if name_to_output == "e":
raise ValueError(
"Attempting to write variable with name 'e' in a CPLEX LP "
"formatted file will cause a parse failure due to confusion with "
"numeric values expressed in scientific notation")
output_file.write(name_to_output)
if vardata.has_ub():
output_file.write(ub_string_template %
(_no_negative_zero(vardata_ub)))
else:
output_file.write(" <= +inf\n")
if len(integer_vars) > 0:
output_file.write("general\n")
for var_name in integer_vars:
output_file.write(' %s\n' % var_name)
if len(binary_vars) > 0:
output_file.write("binary\n")
for var_name in binary_vars:
output_file.write(' %s\n' % var_name)
# Write the SOS section
output_file.write(SOSlines.getvalue())
#
# wrap-up
#
output_file.write("end\n")
# Clean up the symbol map to only contain variables referenced
# in the active constraints **Note**: warm start method may
# rely on this for choosing the set of potential warm start
# variables
vars_to_delete = set(variable_symbol_map.byObject.keys()) - \
set(self._referenced_variable_ids.keys())
sm_byObject = symbol_map.byObject
sm_bySymbol = symbol_map.bySymbol
var_sm_byObject = variable_symbol_map.byObject
for varid in vars_to_delete:
symbol = var_sm_byObject[varid]
del sm_byObject[varid]
del sm_bySymbol[symbol]
del variable_symbol_map
return symbol_map
class CPLEXPersistent(CPLEXDirect, PersistentSolver):
"""The CPLEX LP/MIP solver
"""
pyomo.util.plugin.alias('_cplex_persistent',
doc='Persistent Python interface to the CPLEX LP/MIP solver')
def __init__(self, **kwds):
#
# Call base class constructor
#
kwds['type'] = 'cplexpersistent'
CPLEXDirect.__init__(self, **kwds)
# maps pyomo var data labels to the corresponding CPLEX variable id.
self._cplex_variable_ids = {}
self._cplex_variable_names = None
#
# updates all variable bounds in the compiled model - handles
# fixed variables and related issues. re-does everything from
# scratch by default, ignoring whatever was specified
# previously. if the value associated with the keyword
# vars_to_update is a non-empty list (assumed to be variable name
# / index pairs), then only the bounds for those variables are
# updated. this function assumes that the variables themselves
# already exist in the compiled model.
#
def compile_variable_bounds(self, pyomo_instance, vars_to_update):
from pyomo.core.base import Var
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot compile variable bounds - no "
"instance is presently compiled")
# the bound update entries should be name-value pairs
new_lower_bounds = []
new_upper_bounds = []
# operates through side effects on the above lists!
def update_bounds_lists(var_name):
var_lb = None
var_ub = None
if var_data.fixed and self._output_fixed_variable_bounds:
var_lb = var_ub = var_data.value
elif var_data.fixed:
# if we've been directed to not deal with fixed
# variables, then skip - they should have been
# compiled out of any description of the constraints
return
else:
if not var_data.has_lb():
var_lb = -CPLEXDirect._cplex_module.infinity
else:
var_lb = value(var_data.lb)
if not var_data.has_ub():
var_ub = CPLEXDirect._cplex_module.infinity
else:
var_ub= value(var_data.ub)
var_cplex_id = self._cplex_variable_ids[var_name]
new_lower_bounds.append((var_cplex_id, var_lb))
new_upper_bounds.append((var_cplex_id, var_ub))
if len(vars_to_update) == 0:
for var_data in pyomo_instance.component_data_objects(Var, active=True):
var_name = self._symbol_map.getSymbol(var_data, self._labeler)
update_bounds_lists(var_name)
else:
for var_name, var_index in vars_to_update:
var = pyomo_instance.find_component(var_name)
# TBD - do some error checking!
var_data = var[var_index]
var_name = self._symbol_map.getSymbol(var_data, self._labeler)
update_bounds_lists(var_name)
self._active_cplex_instance.variables.set_lower_bounds(new_lower_bounds)
self._active_cplex_instance.variables.set_upper_bounds(new_upper_bounds)
#
# method to compile objective of the input pyomo instance.
# TBD:
# it may be smarter just to track the associated pyomo instance,
# and re-compile it automatically from a cached local attribute.
# this would ensure consistency, among other things!
#
def compile_objective(self, pyomo_instance):
from pyomo.core.base import Objective
from pyomo.repn import canonical_is_constant, LinearCanonicalRepn, canonical_degree
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot compile objective - no "
"instance is presently compiled")
cplex_instance = self._active_cplex_instance
self._has_quadratic_objective = False
cntr = 0
for block in pyomo_instance.block_data_objects(active=True):
gen_obj_canonical_repn = \
getattr(block, "_gen_obj_canonical_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block,'_canonical_repn'):
block._canonical_repn = ComponentMap()
block_canonical_repn = block._canonical_repn
for obj_data in block.component_data_objects(Objective,
active=True,
descend_into=False):
cntr += 1
if cntr > 1:
raise ValueError(
"Multiple active objectives found on Pyomo instance '%s'. "
"Solver '%s' will only handle a single active objective" \
% (pyomo_instance.name, self.type))
if obj_data.is_minimizing():
cplex_instance.objective.set_sense(
cplex_instance.objective.sense.minimize)
else:
cplex_instance.objective.set_sense(
cplex_instance.objective.sense.maximize)
cplex_instance.objective.set_name(
self._symbol_map.getSymbol(obj_data,
self._labeler))
if gen_obj_canonical_repn:
obj_repn = generate_canonical_repn(obj_data.expr)
block_canonical_repn[obj_data] = obj_repn
else:
obj_repn = block_canonical_repn[obj_data]
if (isinstance(obj_repn, LinearCanonicalRepn) and \
((obj_repn.linear == None) or \
(len(obj_repn.linear) == 0))) or \
canonical_is_constant(obj_repn):
print("Warning: Constant objective detected, replacing "
"with a placeholder to prevent solver failure.")
offset = obj_repn.constant
if offset is None:
offset = 0.0
objective_expression = [("ONE_VAR_CONSTANT",offset)]
cplex_instance.objective.set_linear(objective_expression)
else:
if isinstance(obj_repn, LinearCanonicalRepn):
objective_expression, offset = \
self._encode_constraint_body_linear_specialized(
obj_repn,
self._labeler,
use_variable_names=False,
cplex_variable_name_index_map=self._cplex_variable_ids,
as_pairs=True)
if offset != 0.0:
objective_expression.append((self._cplex_variable_ids["ONE_VAR_CONSTANT"],offset))
cplex_instance.objective.set_linear(objective_expression)
else:
#Linear terms
if 1 in obj_repn:
objective_expression, offset = \
self._encode_constraint_body_linear(
obj_repn,
self._labeler,
as_pairs=True)
if offset != 0.0:
objective_expression.append(("ONE_VAR_CONSTANT",offset))
cplex_instance.objective.set_linear(objective_expression)
#Quadratic terms
if 2 in obj_repn:
self._has_quadratic_objective = True
objective_expression = \
self._encode_constraint_body_quadratic(obj_repn,
self._labeler,
as_triples=True,
is_obj=2.0)
cplex_instance.objective.\
set_quadratic_coefficients(objective_expression)
degree = canonical_degree(obj_repn)
if (degree is None) or (degree > 2):
raise ValueError(
"CPLEXPersistent plugin does not support general nonlinear "
"objective expressions (only linear or quadratic).\n"
"Objective: %s" % (obj_data.name))
#
# method to populate the CPLEX problem instance (interface) from
# the supplied Pyomo problem instance.
#
def compile_instance(self,
pyomo_instance,
symbolic_solver_labels=False,
output_fixed_variable_bounds=False,
skip_trivial_constraints=False):
from pyomo.core.base import Var, Constraint, SOSConstraint
from pyomo.repn import canonical_is_constant, LinearCanonicalRepn, canonical_degree
self._symbolic_solver_labels = symbolic_solver_labels
self._output_fixed_variable_bounds = output_fixed_variable_bounds
self._skip_trivial_constraints = skip_trivial_constraints
self._has_quadratic_constraints = False
self._has_quadratic_objective = False
self._active_cplex_instance = CPLEXDirect._cplex_module.Cplex()
if self._symbolic_solver_labels:
labeler = self._labeler = TextLabeler()
else:
labeler = self._labeler = NumericLabeler('x')
self._symbol_map = SymbolMap()
self._instance = pyomo_instance
if isinstance(pyomo_instance, IBlockStorage):
# BIG HACK
if not hasattr(pyomo_instance, "._symbol_maps"):
setattr(pyomo_instance, "._symbol_maps", {})
getattr(pyomo_instance, "._symbol_maps")[id(self._symbol_map)] = \
self._symbol_map
else:
pyomo_instance.solutions.add_symbol_map(self._symbol_map)
self._smap_id = id(self._symbol_map)
# we use this when iterating over the constraints because it
# will have a much smaller hash table, we also use this for
# the warm start code after it is cleaned to only contain
# variables referenced in the constraints
self._variable_symbol_map = SymbolMap()
# cplex wants the caller to set the problem type, which is (for
# current purposes) strictly based on variable type counts.
self._num_binary_variables = 0
self._num_integer_variables = 0
self._num_continuous_variables = 0
self._used_sos_constraints = False
#############################################
# populate the variables in the cplex model #
#############################################
var_names = []
var_lbs = []
var_ubs = []
var_types = []
self._referenced_variable_ids.clear()
# maps pyomo var data labels to the corresponding CPLEX variable id.
self._cplex_variable_ids.clear()
# cached in the loop below - used to update the symbol map
# immediately following loop termination.
var_label_pairs = []
for var_data in pyomo_instance.component_data_objects(Var, active=True):
if var_data.fixed and not self._output_fixed_variable_bounds:
# if a variable is fixed, and we're preprocessing
# fixed variables (as in not outputting them), there
# is no need to add them to the compiled model.
continue
var_name = self._symbol_map.getSymbol(var_data, labeler)
var_names.append(var_name)
var_label_pairs.append((var_data, var_name))
self._cplex_variable_ids[var_name] = len(self._cplex_variable_ids)
if not var_data.has_lb():
var_lbs.append(-CPLEXDirect._cplex_module.infinity)
else:
var_lbs.append(value(var_data.lb))
if not var_data.has_ub():
var_ubs.append(CPLEXDirect._cplex_module.infinity)
else:
var_ubs.append(value(var_data.ub))
if var_data.is_integer():
var_types.append(self._active_cplex_instance.variables.type.integer)
self._num_integer_variables += 1
elif var_data.is_binary():
var_types.append(self._active_cplex_instance.variables.type.binary)
self._num_binary_variables += 1
elif var_data.is_continuous():
var_types.append(self._active_cplex_instance.variables.type.continuous)
self._num_continuous_variables += 1
else:
raise TypeError("Invalid domain type for variable with name '%s'. "
"Variable is not continuous, integer, or binary.")
self._active_cplex_instance.variables.add(names=var_names,
lb=var_lbs,
ub=var_ubs,
types=var_types)
self._active_cplex_instance.variables.add(lb=[1],
ub=[1],
names=["ONE_VAR_CONSTANT"])
self._cplex_variable_ids["ONE_VAR_CONSTANT"] = len(self._cplex_variable_ids)
self._variable_symbol_map.addSymbols(var_label_pairs)
self._cplex_variable_names = self._active_cplex_instance.variables.get_names()
########################################################
# populate the standard constraints in the cplex model #
########################################################
expressions = []
senses = []
rhss = []
range_values = []
names = []
qexpressions = []
qlinears = []
qsenses = []
qrhss = []
qnames = []
for block in pyomo_instance.block_data_objects(active=True):
gen_con_canonical_repn = \
getattr(block, "_gen_con_canonical_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block,'_canonical_repn'):
block._canonical_repn = ComponentMap()
block_canonical_repn = block._canonical_repn
for con in block.component_data_objects(Constraint,
active=True,
descend_into=False):
if (not con.has_lb()) and \
(not con.has_ub()):
assert not con.equality
continue # not binding at all, don't bother
con_repn = None
if con._linear_canonical_form:
con_repn = con.canonical_form()
elif isinstance(con, LinearCanonicalRepn):
con_repn = con
else:
if gen_con_canonical_repn:
con_repn = generate_canonical_repn(con.body)
block_canonical_repn[con] = con_repn
else:
con_repn = block_canonical_repn[con]
# There are conditions, e.g., when fixing variables, under which
# a constraint block might be empty. Ignore these, for both
# practical reasons and the fact that the CPLEX LP format
# requires a variable in the constraint body. It is also
# possible that the body of the constraint consists of only a
# constant, in which case the "variable" of
if isinstance(con_repn, LinearCanonicalRepn):
if self._skip_trivial_constraints and \
((con_repn.linear is None) or \
(len(con_repn.linear) == 0)):
continue
else:
# we shouldn't come across a constant canonical repn
# that is not LinearCanonicalRepn
assert not canonical_is_constant(con_repn)
name = self._symbol_map.getSymbol(con, labeler)
expr = None
qexpr = None
quadratic = False
if isinstance(con_repn, LinearCanonicalRepn):
expr, offset = \
self._encode_constraint_body_linear_specialized(con_repn,
labeler,
use_variable_names=False,
cplex_variable_name_index_map=self._cplex_variable_ids)
else:
degree = canonical_degree(con_repn)
if degree == 2:
quadratic = True
elif (degree != 0) or (degree != 1):
raise ValueError(
"CPLEXPersistent plugin does not support general nonlinear "
"constraint expression (only linear or quadratic).\n"
"Constraint: %s" % (con.name))
expr, offset = self._encode_constraint_body_linear(con_repn,
labeler)
if quadratic:
if expr is None:
expr = CPLEXDirect._cplex_module.SparsePair(ind=[0],val=[0.0])
self._has_quadratic_constraints = True
qexpr = self._encode_constraint_body_quadratic(con_repn,labeler)
qnames.append(name)
if con.equality:
# equality constraint.
qsenses.append('E')
qrhss.append(self._get_bound(con.lower) - offset)
elif con.has_lb() and con.has_ub():
raise RuntimeError(
"The CPLEXDirect plugin can not translate range "
"constraints containing quadratic expressions.")
elif con.has_lb():
assert not con.has_ub()
qsenses.append('G')
qrhss.append(self._get_bound(con.lower) - offset)
else:
assert con.has_ub()
qsenses.append('L')
qrhss.append(self._get_bound(con.upper) - offset)
qlinears.append(expr)
qexpressions.append(qexpr)
else:
names.append(name)
expressions.append(expr)
if con.equality:
# equality constraint.
senses.append('E')
rhss.append(self._get_bound(con.lower) - offset)
range_values.append(0.0)
elif con.has_lb() and con.has_ub():
# ranged constraint.
senses.append('R')
lower_bound = self._get_bound(con.lower) - offset
upper_bound = self._get_bound(con.upper) - offset
rhss.append(lower_bound)
range_values.append(upper_bound - lower_bound)
elif con.has_lb():
senses.append('G')
rhss.append(self._get_bound(con.lower) - offset)
range_values.append(0.0)
else:
assert con.has_ub()
senses.append('L')
rhss.append(self._get_bound(con.upper) - offset)
range_values.append(0.0)
###################################################
# populate the SOS constraints in the cplex model #
###################################################
# SOS constraints - largely taken from cpxlp.py so updates there,
# should be applied here
# TODO: Allow users to specify the variables coefficients for custom
# branching/set orders - refer to cpxlp.py
sosn = self._capabilities.sosn
sos1 = self._capabilities.sos1
sos2 = self._capabilities.sos2
modelSOS = ModelSOS()
for soscondata in pyomo_instance.component_data_objects(SOSConstraint,
active=True):
level = soscondata.level
if (level == 1 and not sos1) or \
(level == 2 and not sos2) or \
(level > 2 and not sosn):
raise Exception("Solver does not support SOS level %s constraints"
% (level,))
modelSOS.count_constraint(self._symbol_map,
labeler,
self._variable_symbol_map,
soscondata)
if modelSOS.sosType:
for key in modelSOS.sosType:
self._active_cplex_instance.SOS.add(type = modelSOS.sosType[key],
name = modelSOS.sosName[key],
SOS = [modelSOS.varnames[key],
modelSOS.weights[key]])
self._referenced_variable_ids.update(modelSOS.varids[key])
self._used_sos_constraints = True
self._active_cplex_instance.linear_constraints.add(
lin_expr=expressions,
senses=senses,
rhs=rhss,
range_values=range_values,
names=names)
for index in xrange(len(qexpressions)):
self._active_cplex_instance.quadratic_constraints.add(
lin_expr=qlinears[index],
quad_expr=qexpressions[index],
sense=qsenses[index],
rhs=qrhss[index],
name=qnames[index])
#############################################
# populate the objective in the cplex model #
#############################################
self.compile_objective(pyomo_instance)
#
# simple method to query whether a Pyomo instance has already been
# compiled.
#
def instance_compiled(self):
return self._active_cplex_instance is not None
#
# Override base class method to check for compiled instance
#
def _warm_start(self, instance):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot warm start - no instance is "
"presently compiled")
# clear any existing warm starts.
self._active_cplex_instance.MIP_starts.delete()
# the iteration order is identical to that used in generating
# the cplex instance, so all should be well.
variable_ids = []
variable_values = []
# IMPT: the var_data returned is a weak ref!
for label, var_data in iteritems(self._variable_symbol_map.bySymbol):
cplex_id = self._cplex_variable_ids[label]
if var_data().fixed and not self._output_fixed_variable_bounds:
continue
elif var_data().value is not None:
variable_ids.append(cplex_id)
variable_values.append(var_data().value)
if len(variable_ids):
self._active_cplex_instance.MIP_starts.add(
[variable_ids, variable_values],
self._active_cplex_instance.MIP_starts.effort_level.auto)
#
# Override base class method to check for compiled instance
#
def _populate_cplex_instance(self, model):
assert model == self._instance
def _presolve(self, *args, **kwds):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin"
" cannot presolve - no instance is "
"presently compiled")
# These must be passed in to the compile_instance method,
# but assert that any values here match those already supplied
if 'symbolic_solver_labels' in kwds:
assert self._symbolic_solver_labels == \
kwds['symbolic_solver_labels']
if 'output_fixed_variable_bounds' in kwds:
assert self._output_fixed_variable_bounds == \
kwds['output_fixed_variable_bounds']
if 'skip_trivial_constraints' in kwds:
assert self._skip_trivial_constraints == \
kwds["skip_trivial_constraints"]
if isinstance(self._instance, IBlockStorage):
# BIG HACK
if not hasattr(self._instance, "._symbol_maps"):
setattr(self._instance, "._symbol_maps", {})
getattr(self._instance, "._symbol_maps")[id(self._symbol_map)] = \
self._symbol_map
else:
if self._smap_id not in self._instance.solutions.symbol_map:
self._instance.solutions.add_symbol_map(self._symbol_map)
################################################
# populate the problem type in the cplex model #
################################################
# This gets rid of the annoying "Freeing MIP data." message.
def _filter_freeing_mip_data(val):
if val.strip() == 'Freeing MIP data.':
return ""
return val
self._active_cplex_instance.set_warning_stream(sys.stderr,
fn=_filter_freeing_mip_data)
if (self._has_quadratic_objective is True) or \
(self._has_quadratic_constraints is True):
if (self._num_integer_variables > 0) or \
(self._num_binary_variables > 0) or \
(self._used_sos_constraints):
if self._has_quadratic_constraints is True:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.MIQCP)
else:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.MIQP)
else:
if self._has_quadratic_constraints is True:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.QCP)
else:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.QP)
elif (self._num_integer_variables > 0) or \
(self._num_binary_variables > 0) or \
(self._used_sos_constraints):
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.MILP)
else:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.LP)
# restore the warning stream without our filter function
self._active_cplex_instance.set_warning_stream(sys.stderr)
CPLEXDirect._presolve(self, *args, **kwds)
# like other solver plugins, persistent solver plugins can
# take an instance as an input argument. the only context in
# which this instance is used, however, is for warm-starting.
if len(args) > 2:
raise ValueError("The CPLEXPersistent plugin method "
"'_presolve' can be supplied at most "
"one problem instance - %s were "
"supplied" % len(args))
# Re-add the symbol map id if it was cleared
# after a previous solution load
if id(self._symbol_map) not in args[0].solutions.symbol_map:
args[0].solutions.add_symbol_map(self._symbol_map)
self._smap_id = id(self._symbol_map)
#
# invoke the solver on the currently compiled instance!!!
#
def _apply_solver(self):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin cannot "
"apply solver - no instance is presently compiled")
# NOTE:
# CPLEX maintains the pool of feasible solutions from the
# prior solve as the set of mip starts for the next solve.
# and evaluating multiple mip starts (and there can be many)
# is expensive. so if the warm_start method is not invoked,
# there will potentially be a lot of time wasted.
return CPLEXDirect._apply_solver(self)
def _postsolve(self):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot postsolve - no instance is "
"presently compiled")
active_cplex_instance = self._active_cplex_instance
variable_symbol_map = self._variable_symbol_map
instance = self._instance
ret = CPLEXDirect._postsolve(self)
#
# These get reset to None by the base class method
#
self._active_cplex_instance = active_cplex_instance
self._variable_symbol_map = variable_symbol_map
self._instance = instance
return ret
def _print_model_MPS(self,
model,
output_file,
solver_capability,
labeler,
output_fixed_variable_bounds=False,
file_determinism=1,
row_order=None,
column_order=None,
skip_trivial_constraints=False,
force_objective_constant=False,
include_all_variable_bounds=False,
skip_objective_sense=False):
symbol_map = SymbolMap()
variable_symbol_map = SymbolMap()
# NOTE: we use createSymbol instead of getSymbol because we
# know whether or not the symbol exists, and don't want
# to the overhead of error/duplicate checking.
# cache frequently called functions
extract_variable_coefficients = self._extract_variable_coefficients
create_symbol_func = SymbolMap.createSymbol
create_symbols_func = SymbolMap.createSymbols
alias_symbol_func = SymbolMap.alias
variable_label_pairs = []
sortOrder = SortComponents.unsorted
if file_determinism >= 1:
sortOrder = sortOrder | SortComponents.indices
if file_determinism >= 2:
sortOrder = sortOrder | SortComponents.alphabetical
#
# Create variable symbols (and cache the block list)
#
all_blocks = []
variable_list = []
for block in model.block_data_objects(active=True, sort=sortOrder):
all_blocks.append(block)
for vardata in block.component_data_objects(Var,
active=True,
sort=sortOrder,
descend_into=False):
variable_list.append(vardata)
variable_label_pairs.append(
(vardata, create_symbol_func(symbol_map, vardata,
labeler)))
variable_symbol_map.addSymbols(variable_label_pairs)
# and extract the information we'll need for rapid labeling.
object_symbol_dictionary = symbol_map.byObject
variable_symbol_dictionary = variable_symbol_map.byObject
# sort the variable ordering by the user
# column_order ComponentMap
if column_order is not None:
variable_list.sort(key=lambda _x: column_order[_x])
# prepare to hold the sparse columns
variable_to_column = ComponentMap(
(vardata, i) for i, vardata in enumerate(variable_list))
# add one position for ONE_VAR_CONSTANT
column_data = [[] for i in xrange(len(variable_list) + 1)]
quadobj_data = []
quadmatrix_data = []
# constraint rhs
rhs_data = []
# print the model name and the source, so we know
# roughly where
output_file.write("* Source: Pyomo MPS Writer\n")
output_file.write("* Format: Free MPS\n")
output_file.write("*\n")
output_file.write("NAME %s\n" % (model.name, ))
#
# ROWS section
#
objective_label = None
numObj = 0
onames = []
for block in all_blocks:
gen_obj_repn = \
getattr(block, "_gen_obj_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block, '_repn'):
block._repn = ComponentMap()
block_repn = block._repn
for objective_data in block.component_data_objects(
Objective, active=True, sort=sortOrder,
descend_into=False):
numObj += 1
onames.append(objective_data.name)
if numObj > 1:
raise ValueError(
"More than one active objective defined for input "
"model '%s'; Cannot write legal MPS file\n"
"Objectives: %s" % (model.name, ' '.join(onames)))
objective_label = create_symbol_func(symbol_map,
objective_data, labeler)
symbol_map.alias(objective_data, '__default_objective__')
if not skip_objective_sense:
output_file.write("OBJSENSE\n")
if objective_data.is_minimizing():
output_file.write(" MIN\n")
else:
output_file.write(" MAX\n")
# This section is not recognized by the COIN-OR
# MPS reader
#output_file.write("OBJNAME\n")
#output_file.write(" %s\n" % (objective_label))
output_file.write("ROWS\n")
output_file.write(" N %s\n" % (objective_label))
if gen_obj_repn:
repn = \
generate_standard_repn(objective_data.expr)
block_repn[objective_data] = repn
else:
repn = block_repn[objective_data]
degree = repn.polynomial_degree()
if degree == 0:
logger.warning(
"Constant objective detected, replacing "
"with a placeholder to prevent solver failure.")
force_objective_constant = True
elif degree is None:
raise RuntimeError(
"Cannot write legal MPS file. Objective '%s' "
"has nonlinear terms that are not quadratic." %
objective_data.name)
constant = extract_variable_coefficients(
objective_label, repn, column_data, quadobj_data,
variable_to_column)
if force_objective_constant or (constant != 0.0):
# ONE_VAR_CONSTANT
column_data[-1].append((objective_label, constant))
if numObj == 0:
raise ValueError(
"Cannot write legal MPS file: No objective defined "
"for input model '%s'." % str(model))
assert objective_label is not None
# Constraints
def constraint_generator():
for block in all_blocks:
gen_con_repn = \
getattr(block, "_gen_con_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block, '_repn'):
block._repn = ComponentMap()
block_repn = block._repn
for constraint_data in block.component_data_objects(
Constraint,
active=True,
sort=sortOrder,
descend_into=False):
if (not constraint_data.has_lb()) and \
(not constraint_data.has_ub()):
assert not constraint_data.equality
continue # non-binding, so skip
if constraint_data._linear_canonical_form:
repn = constraint_data.canonical_form()
elif gen_con_repn:
repn = generate_standard_repn(constraint_data.body)
block_repn[constraint_data] = repn
else:
repn = block_repn[constraint_data]
yield constraint_data, repn
if row_order is not None:
sorted_constraint_list = list(constraint_generator())
sorted_constraint_list.sort(key=lambda x: row_order[x[0]])
def yield_all_constraints():
for constraint_data, repn in sorted_constraint_list:
yield constraint_data, repn
else:
yield_all_constraints = constraint_generator
for constraint_data, repn in yield_all_constraints():
degree = repn.polynomial_degree()
# Write constraint
if degree == 0:
if skip_trivial_constraints:
continue
elif degree is None:
raise RuntimeError(
"Cannot write legal MPS file. Constraint '%s' "
"has nonlinear terms that are not quadratic." %
constraint_data.name)
# Create symbol
con_symbol = create_symbol_func(symbol_map, constraint_data,
labeler)
if constraint_data.equality:
assert value(constraint_data.lower) == \
value(constraint_data.upper)
label = 'c_e_' + con_symbol + '_'
alias_symbol_func(symbol_map, constraint_data, label)
output_file.write(" E %s\n" % (label))
offset = extract_variable_coefficients(label, repn,
column_data,
quadmatrix_data,
variable_to_column)
bound = constraint_data.lower
bound = _get_bound(bound) - offset
rhs_data.append((label, _no_negative_zero(bound)))
else:
if constraint_data.has_lb():
if constraint_data.has_ub():
label = 'r_l_' + con_symbol + '_'
else:
label = 'c_l_' + con_symbol + '_'
alias_symbol_func(symbol_map, constraint_data, label)
output_file.write(" G %s\n" % (label))
offset = extract_variable_coefficients(
label, repn, column_data, quadmatrix_data,
variable_to_column)
bound = constraint_data.lower
bound = _get_bound(bound) - offset
rhs_data.append((label, _no_negative_zero(bound)))
else:
assert constraint_data.has_ub()
if constraint_data.has_ub():
if constraint_data.has_lb():
label = 'r_u_' + con_symbol + '_'
else:
label = 'c_u_' + con_symbol + '_'
alias_symbol_func(symbol_map, constraint_data, label)
output_file.write(" L %s\n" % (label))
offset = extract_variable_coefficients(
label, repn, column_data, quadmatrix_data,
variable_to_column)
bound = constraint_data.upper
bound = _get_bound(bound) - offset
rhs_data.append((label, _no_negative_zero(bound)))
else:
assert constraint_data.has_lb()
if len(column_data[-1]) > 0:
# ONE_VAR_CONSTANT = 1
output_file.write(" E c_e_ONE_VAR_CONSTANT\n")
column_data[-1].append(("c_e_ONE_VAR_CONSTANT", 1))
rhs_data.append(("c_e_ONE_VAR_CONSTANT", 1))
#
# COLUMNS section
#
column_template = " %s %s %" + self._precision_string + "\n"
output_file.write("COLUMNS\n")
cnt = 0
for vardata in variable_list:
col_entries = column_data[variable_to_column[vardata]]
cnt += 1
if len(col_entries) > 0:
var_label = variable_symbol_dictionary[id(vardata)]
for i, (row_label, coef) in enumerate(col_entries):
output_file.write(
column_template %
(var_label, row_label, _no_negative_zero(coef)))
elif include_all_variable_bounds:
# the column is empty, so add a (0 * var)
# term to the objective
# * Note that some solvers (e.g., Gurobi)
# will accept an empty column as a line
# with just the column name. This doesn't
# seem to work for CPLEX 12.6, so I am
# doing it this way so that it will work for both
var_label = variable_symbol_dictionary[id(vardata)]
output_file.write(column_template %
(var_label, objective_label, 0))
assert cnt == len(column_data) - 1
if len(column_data[-1]) > 0:
col_entries = column_data[-1]
var_label = "ONE_VAR_CONSTANT"
for i, (row_label, coef) in enumerate(col_entries):
output_file.write(
column_template %
(var_label, row_label, _no_negative_zero(coef)))
#
# RHS section
#
rhs_template = " RHS %s %" + self._precision_string + "\n"
output_file.write("RHS\n")
for i, (row_label, rhs) in enumerate(rhs_data):
# note: we have already converted any -0 to 0 by this point
output_file.write(rhs_template % (row_label, rhs))
# SOS constraints
SOSlines = StringIO()
sos1 = solver_capability("sos1")
sos2 = solver_capability("sos2")
for block in all_blocks:
for soscondata in block.component_data_objects(SOSConstraint,
active=True,
sort=sortOrder,
descend_into=False):
create_symbol_func(symbol_map, soscondata, labeler)
level = soscondata.level
if (level == 1 and not sos1) or \
(level == 2 and not sos2) or \
(level > 2):
raise ValueError(
"Solver does not support SOS level %s constraints" %
(level))
# This updates the referenced_variable_ids, just in case
# there is a variable that only appears in an
# SOSConstraint, in which case this needs to be known
# before we write the "bounds" section (Cplex does not
# handle this correctly, Gurobi does)
self._printSOS(symbol_map, labeler, variable_symbol_map,
soscondata, SOSlines)
#
# BOUNDS section
#
entry_template = "%s %" + self._precision_string + "\n"
output_file.write("BOUNDS\n")
for vardata in variable_list:
if include_all_variable_bounds or \
(id(vardata) in self._referenced_variable_ids):
var_label = variable_symbol_dictionary[id(vardata)]
if vardata.fixed:
if not 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 MPS file." % (vardata.name, model.name))
if vardata.value is None:
raise ValueError(
"Variable cannot be fixed to a value of None.")
output_file.write(
(" FX BOUND " + entry_template) %
(var_label, _no_negative_zero(value(vardata.value))))
continue
# convert any -0 to 0 to make baseline diffing easier
vardata_lb = _no_negative_zero(_get_bound(vardata.lb))
vardata_ub = _no_negative_zero(_get_bound(vardata.ub))
unbounded_lb = not vardata.has_lb()
unbounded_ub = not vardata.has_ub()
treat_as_integer = False
if vardata.is_binary():
if (vardata_lb == 0) and (vardata_ub == 1):
output_file.write(" BV BOUND %s\n" % (var_label))
continue
else:
# so we can add bounds
treat_as_integer = True
if treat_as_integer or vardata.is_integer():
# Indicating unbounded integers is tricky because
# the only way to indicate a variable is integer
# is using the bounds section. Thus, we signify
# infinity with a large number (10E20)
# * Note: Gurobi allows values like inf and -inf
# but CPLEX 12.6 does not, so I am just
# using a large value
if not unbounded_lb:
output_file.write((" LI BOUND " + entry_template) %
(var_label, vardata_lb))
else:
output_file.write(" LI BOUND %s -10E20\n" %
(var_label))
if not unbounded_ub:
output_file.write((" UI BOUND " + entry_template) %
(var_label, vardata_ub))
else:
output_file.write(" UI BOUND %s 10E20\n" % (var_label))
else:
assert vardata.is_continuous()
if unbounded_lb and unbounded_ub:
output_file.write(" FR BOUND %s\n" % (var_label))
else:
if not unbounded_lb:
output_file.write((" LO BOUND " + entry_template) %
(var_label, vardata_lb))
else:
output_file.write(" MI BOUND %s\n" % (var_label))
if not unbounded_ub:
output_file.write((" UP BOUND " + entry_template) %
(var_label, vardata_ub))
#
# SOS section
#
output_file.write(SOSlines.getvalue())
# Formatting of the next two sections comes from looking
# at Gurobi and Cplex output
#
# QUADOBJ section
#
if len(quadobj_data) > 0:
assert len(quadobj_data) == 1
# it looks like the COIN-OR MPS Reader only
# recognizes QUADOBJ (Gurobi and Cplex seem to
# be okay with this)
output_file.write("QUADOBJ\n")
#output_file.write("QMATRIX\n")
label, quad_terms = quadobj_data[0]
assert label == objective_label
# sort by the sorted tuple of symbols (or column assignments)
# for the variables appearing in the term
quad_terms = sorted(quad_terms,
key=lambda _x: \
sorted((variable_to_column[_x[0][0]],
variable_to_column[_x[0][1]])))
for term, coef in quad_terms:
# sort the term for consistent output
var1, var2 = sorted(term,
key=lambda _x: variable_to_column[_x])
var1_label = variable_symbol_dictionary[id(var1)]
var2_label = variable_symbol_dictionary[id(var2)]
# Don't forget that a quadratic objective is always
# assumed to be divided by 2
if var1_label == var2_label:
output_file.write(
column_template %
(var1_label, var2_label, _no_negative_zero(coef * 2)))
else:
# the matrix needs to be symmetric so split
# the coefficient (but remember it is divided by 2)
output_file.write(
column_template %
(var1_label, var2_label, _no_negative_zero(coef)))
output_file.write(
column_template %
(var2_label, var1_label, _no_negative_zero(coef)))
#
# QCMATRIX section
#
if len(quadmatrix_data) > 0:
for row_label, quad_terms in quadmatrix_data:
output_file.write("QCMATRIX %s\n" % (row_label))
# sort by the sorted tuple of symbols (or
# column assignments) for the variables
# appearing in the term
quad_terms = sorted(quad_terms,
key=lambda _x: \
sorted((variable_to_column[_x[0][0]],
variable_to_column[_x[0][1]])))
for term, coef in quad_terms:
# sort the term for consistent output
var1, var2 = sorted(term,
key=lambda _x: variable_to_column[_x])
var1_label = variable_symbol_dictionary[id(var1)]
var2_label = variable_symbol_dictionary[id(var2)]
if var1_label == var2_label:
output_file.write(
column_template %
(var1_label, var2_label, _no_negative_zero(coef)))
else:
# the matrix needs to be symmetric so split
# the coefficient
output_file.write(column_template %
(var1_label, var2_label,
_no_negative_zero(coef * 0.5)))
output_file.write(column_template %
(var2_label, var1_label, coef * 0.5))
output_file.write("ENDATA\n")
# Clean up the symbol map to only contain variables referenced
# in the active constraints **Note**: warm start method may
# rely on this for choosing the set of potential warm start
# variables
vars_to_delete = set(variable_symbol_map.byObject.keys()) - \
set(self._referenced_variable_ids.keys())
sm_byObject = symbol_map.byObject
sm_bySymbol = symbol_map.bySymbol
var_sm_byObject = variable_symbol_map.byObject
for varid in vars_to_delete:
symbol = var_sm_byObject[varid]
del sm_byObject[varid]
del sm_bySymbol[symbol]
del variable_symbol_map
return symbol_map
def _print_model_MPS(self,
model,
output_file,
solver_capability,
labeler,
output_fixed_variable_bounds=False,
file_determinism=1,
row_order=None,
column_order=None,
skip_trivial_constraints=False,
force_objective_constant=False,
include_all_variable_bounds=False,
skip_objective_sense=False):
symbol_map = SymbolMap()
variable_symbol_map = SymbolMap()
# NOTE: we use createSymbol instead of getSymbol because we
# know whether or not the symbol exists, and don't want
# to the overhead of error/duplicate checking.
# cache frequently called functions
extract_variable_coefficients = self._extract_variable_coefficients
create_symbol_func = SymbolMap.createSymbol
create_symbols_func = SymbolMap.createSymbols
alias_symbol_func = SymbolMap.alias
variable_label_pairs = []
sortOrder = SortComponents.unsorted
if file_determinism >= 1:
sortOrder = sortOrder | SortComponents.indices
if file_determinism >= 2:
sortOrder = sortOrder | SortComponents.alphabetical
#
# Create variable symbols (and cache the block list)
#
all_blocks = []
variable_list = []
for block in model.block_data_objects(active=True,
sort=sortOrder):
all_blocks.append(block)
for vardata in block.component_data_objects(
Var,
active=True,
sort=sortOrder,
descend_into=False):
variable_list.append(vardata)
variable_label_pairs.append(
(vardata,create_symbol_func(symbol_map,
vardata,
labeler)))
variable_symbol_map.addSymbols(variable_label_pairs)
# and extract the information we'll need for rapid labeling.
object_symbol_dictionary = symbol_map.byObject
variable_symbol_dictionary = variable_symbol_map.byObject
# sort the variable ordering by the user
# column_order ComponentMap
if column_order is not None:
variable_list.sort(key=lambda _x: column_order[_x])
# prepare to hold the sparse columns
variable_to_column = ComponentMap(
(vardata, i) for i, vardata in enumerate(variable_list))
# add one position for ONE_VAR_CONSTANT
column_data = [[] for i in xrange(len(variable_list)+1)]
quadobj_data = []
quadmatrix_data = []
# constraint rhs
rhs_data = []
# print the model name and the source, so we know
# roughly where
output_file.write("* Source: Pyomo MPS Writer\n")
output_file.write("* Format: Free MPS\n")
output_file.write("*\n")
output_file.write("NAME %s\n" % (model.name,))
#
# ROWS section
#
objective_label = None
numObj = 0
onames = []
for block in all_blocks:
gen_obj_canonical_repn = \
getattr(block, "_gen_obj_canonical_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block,'_canonical_repn'):
block._canonical_repn = ComponentMap()
block_canonical_repn = block._canonical_repn
for objective_data in block.component_data_objects(
Objective,
active=True,
sort=sortOrder,
descend_into=False):
numObj += 1
onames.append(objective_data.cname())
if numObj > 1:
raise ValueError(
"More than one active objective defined for input "
"model '%s'; Cannot write legal MPS file\n"
"Objectives: %s" % (model.cname(True), ' '.join(onames)))
objective_label = create_symbol_func(symbol_map,
objective_data,
labeler)
symbol_map.alias(objective_data, '__default_objective__')
if not skip_objective_sense:
output_file.write("OBJSENSE\n")
if objective_data.is_minimizing():
output_file.write(" MIN\n")
else:
output_file.write(" MAX\n")
# This section is not recognized by the COIN-OR
# MPS reader
#output_file.write("OBJNAME\n")
#output_file.write(" %s\n" % (objective_label))
output_file.write("ROWS\n")
output_file.write(" N %s\n" % (objective_label))
if gen_obj_canonical_repn:
canonical_repn = \
generate_canonical_repn(objective_data.expr)
block_canonical_repn[objective_data] = canonical_repn
else:
canonical_repn = block_canonical_repn[objective_data]
degree = canonical_degree(canonical_repn)
if degree == 0:
print("Warning: Constant objective detected, replacing "
"with a placeholder to prevent solver failure.")
force_objective_constant = True
elif (degree != 1) and (degree != 2):
raise RuntimeError(
"Cannot write legal MPS file. Objective '%s' "
"has nonlinear terms that are not quadratic."
% objective_data.cname(True))
constant = extract_variable_coefficients(
objective_label,
canonical_repn,
column_data,
quadobj_data,
variable_to_column)
if force_objective_constant or (constant != 0.0):
# ONE_VAR_CONSTANT
column_data[-1].append((objective_label, constant))
if numObj == 0:
raise ValueError(
"Cannot write legal MPS file: No objective defined "
"for input model '%s'." % str(model))
assert objective_label is not None
# Constraints
def constraint_generator():
for block in all_blocks:
gen_con_canonical_repn = \
getattr(block, "_gen_con_canonical_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block,'_canonical_repn'):
block._canonical_repn = ComponentMap()
block_canonical_repn = block._canonical_repn
for constraint_data in block.component_data_objects(
Constraint,
active=True,
sort=sortOrder,
descend_into=False):
if isinstance(constraint_data, LinearCanonicalRepn):
canonical_repn = constraint_data
else:
if gen_con_canonical_repn:
canonical_repn = generate_canonical_repn(
constraint_data.body)
block_canonical_repn[constraint_data] = canonical_repn
else:
canonical_repn = block_canonical_repn[constraint_data]
yield constraint_data, canonical_repn
if row_order is not None:
sorted_constraint_list = list(constraint_generator())
sorted_constraint_list.sort(key=lambda x: row_order[x[0]])
def yield_all_constraints():
for constraint_data, canonical_repn in sorted_constraint_list:
yield constraint_data, canonical_repn
else:
yield_all_constraints = constraint_generator
for constraint_data, canonical_repn in yield_all_constraints():
degree = canonical_degree(canonical_repn)
# Write constraint
if degree == 0:
if skip_trivial_constraints:
continue
elif (degree != 1) and (degree != 2):
raise RuntimeError(
"Cannot write legal MPS file. Constraint '%s' "
"has nonlinear terms that are not quadratic."
% constraint_data.cname(True))
# Create symbol
con_symbol = create_symbol_func(symbol_map,
constraint_data,
labeler)
if constraint_data.equality:
label = 'c_e_' + con_symbol + '_'
alias_symbol_func(symbol_map, constraint_data, label)
output_file.write(" E %s\n" % (label))
offset = extract_variable_coefficients(
label,
canonical_repn,
column_data,
quadmatrix_data,
variable_to_column)
bound = constraint_data.lower
bound = self._get_bound(bound) - offset
rhs_data.append((label, bound))
else:
if constraint_data.lower is not None:
if constraint_data.upper is not None:
label = 'r_l_' + con_symbol + '_'
else:
label = 'c_l_' + con_symbol + '_'
alias_symbol_func(symbol_map, constraint_data, label)
output_file.write(" G %s\n" % (label))
offset = extract_variable_coefficients(
label,
canonical_repn,
column_data,
quadmatrix_data,
variable_to_column)
bound = constraint_data.lower
bound = self._get_bound(bound) - offset
rhs_data.append((label, bound))
if constraint_data.upper is not None:
if constraint_data.lower is not None:
label = 'r_u_' + con_symbol + '_'
else:
label = 'c_u_' + con_symbol + '_'
alias_symbol_func(symbol_map, constraint_data, label)
output_file.write(" L %s\n" % (label))
offset = extract_variable_coefficients(
label,
canonical_repn,
column_data,
quadmatrix_data,
variable_to_column)
bound = constraint_data.upper
bound = self._get_bound(bound) - offset
rhs_data.append((label, bound))
if len(column_data[-1]) > 0:
# ONE_VAR_CONSTANT = 1
output_file.write(" E c_e_ONE_VAR_CONSTANT\n")
column_data[-1].append(("c_e_ONE_VAR_CONSTANT",1))
rhs_data.append(("c_e_ONE_VAR_CONSTANT",1))
#
# COLUMNS section
#
column_template = " %s %s %"+self._precision_string+"\n"
output_file.write("COLUMNS\n")
cnt = 0
for vardata in variable_list:
col_entries = column_data[variable_to_column[vardata]]
cnt += 1
if len(col_entries) > 0:
var_label = variable_symbol_dictionary[id(vardata)]
for i, (row_label, coef) in enumerate(col_entries):
output_file.write(column_template % (var_label,
row_label,
coef))
elif include_all_variable_bounds:
# the column is empty, so add a (0 * var)
# term to the objective
# * Note that some solvers (e.g., Gurobi)
# will accept an empty column as a line
# with just the column name. This doesn't
# seem to work for CPLEX 12.6, so I am
# doing it this way so that it will work for both
var_label = variable_symbol_dictionary[id(vardata)]
output_file.write(column_template % (var_label,
objective_label,
0))
assert cnt == len(column_data)-1
if len(column_data[-1]) > 0:
col_entries = column_data[-1]
var_label = "ONE_VAR_CONSTANT"
for i, (row_label, coef) in enumerate(col_entries):
output_file.write(column_template % (var_label,
row_label,
coef))
#
# RHS section
#
rhs_template = " RHS %s %"+self._precision_string+"\n"
output_file.write("RHS\n")
for i, (row_label, rhs) in enumerate(rhs_data):
output_file.write(rhs_template % (row_label, rhs))
# SOS constraints
SOSlines = StringIO()
sos1 = solver_capability("sos1")
sos2 = solver_capability("sos2")
for block in all_blocks:
for soscondata in block.component_data_objects(
SOSConstraint,
active=True,
sort=sortOrder,
descend_into=False):
create_symbol_func(symbol_map, soscondata, labeler)
level = soscondata.level
if (level == 1 and not sos1) or \
(level == 2 and not sos2) or \
(level > 2):
raise ValueError(
"Solver does not support SOS level %s constraints" % (level))
# This updates the referenced_variable_ids, just in case
# there is a variable that only appears in an
# SOSConstraint, in which case this needs to be known
# before we write the "bounds" section (Cplex does not
# handle this correctly, Gurobi does)
self._printSOS(symbol_map,
labeler,
variable_symbol_map,
soscondata,
SOSlines)
#
# BOUNDS section
#
entry_template = "%s %"+self._precision_string+"\n"
output_file.write("BOUNDS\n")
for vardata in variable_list:
if include_all_variable_bounds or \
(id(vardata) in self._referenced_variable_ids):
var_label = variable_symbol_dictionary[id(vardata)]
if vardata.fixed:
if not 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 MPS file." % (vardata.cname(True), model.cname(True)))
if vardata.value is None:
raise ValueError("Variable cannot be fixed to a value of None.")
output_file.write((" FX BOUND "+entry_template)
% (var_label, value(vardata.value)))
continue
vardata_lb = self._get_bound(vardata.lb)
vardata_ub = self._get_bound(vardata.ub)
# Make it harder for -0 to show up in
# the output. This makes file diffing
# for test baselines slightly less
# annoying
if vardata_lb == 0:
vardata_lb = 0
if vardata_ub == 0:
vardata_ub = 0
unbounded_lb = (vardata_lb is None) or (vardata_lb == -infinity)
unbounded_ub = (vardata_ub is None) or (vardata_ub == infinity)
treat_as_integer = False
if vardata.is_binary():
if (vardata_lb == 0) and (vardata_ub == 1):
output_file.write(" BV BOUND %s\n" % (var_label))
continue
else:
# so we can add bounds
treat_as_integer = True
if treat_as_integer or vardata.is_integer():
# Indicating unbounded integers is tricky because
# the only way to indicate a variable is integer
# is using the bounds section. Thus, we signify
# infinity with a large number (10E20)
# * Note: Gurobi allows values like inf and -inf
# but CPLEX 12.6 does not, so I am just
# using a large value
if not unbounded_lb:
output_file.write((" LI BOUND "+entry_template)
% (var_label, vardata_lb))
else:
output_file.write(" LI BOUND %s -10E20\n" % (var_label))
if not unbounded_ub:
output_file.write((" UI BOUND "+entry_template)
% (var_label, vardata_ub))
else:
output_file.write(" UI BOUND %s 10E20\n" % (var_label))
else:
assert vardata.is_continuous()
if unbounded_lb and unbounded_ub:
output_file.write(" FR BOUND %s\n" % (var_label))
else:
if not unbounded_lb:
output_file.write((" LO BOUND "+entry_template)
% (var_label, vardata_lb))
else:
output_file.write(" MI BOUND %s\n" % (var_label))
if not unbounded_ub:
output_file.write((" UP BOUND "+entry_template)
% (var_label, vardata_ub))
#
# SOS section
#
output_file.write(SOSlines.getvalue())
# Formatting of the next two sections comes from looking
# at Gurobi and Cplex output
#
# QUADOBJ section
#
if len(quadobj_data) > 0:
assert len(quadobj_data) == 1
# it looks like the COIN-OR MPS Reader only
# recognizes QUADOBJ (Gurobi and Cplex seem to
# be okay with this)
output_file.write("QUADOBJ\n")
#output_file.write("QMATRIX\n")
label, quad_terms = quadobj_data[0]
assert label == objective_label
for (var1, var2), coef in sorted(quad_terms,
key=lambda _x: (variable_to_column[_x[0][0]],
variable_to_column[_x[0][1]])):
var1_label = variable_symbol_dictionary[id(var1)]
var2_label = variable_symbol_dictionary[id(var2)]
# Don't forget that a quadratic objective is always
# assumed to be divided by 2
if var1_label == var2_label:
output_file.write(column_template % (var1_label,
var2_label,
coef * 2))
else:
# the matrix needs to be symmetric so split
# the coefficient (but remember it is divided by 2)
output_file.write(column_template % (var1_label,
var2_label,
coef))
output_file.write(column_template % (var2_label,
var1_label,
coef))
#
# QCMATRIX section
#
if len(quadmatrix_data) > 0:
for row_label, quad_terms in quadmatrix_data:
output_file.write("QCMATRIX %s\n" % (row_label))
for (var1, var2), coef in sorted(quad_terms,
key=lambda _x: (variable_to_column[_x[0][0]],
variable_to_column[_x[0][1]])):
var1_label = variable_symbol_dictionary[id(var1)]
var2_label = variable_symbol_dictionary[id(var2)]
if var1_label == var2_label:
output_file.write(column_template % (var1_label,
var2_label,
coef))
else:
# the matrix needs to be symmetric so split
# the coefficient
output_file.write(column_template % (var1_label,
var2_label,
coef * 0.5))
output_file.write(column_template % (var2_label,
var1_label,
coef * 0.5))
output_file.write("ENDATA\n")
# Clean up the symbol map to only contain variables referenced
# in the active constraints **Note**: warm start method may
# rely on this for choosing the set of potential warm start
# variables
vars_to_delete = set(variable_symbol_map.byObject.keys()) - \
set(self._referenced_variable_ids.keys())
sm_byObject = symbol_map.byObject
sm_bySymbol = symbol_map.bySymbol
var_sm_byObject = variable_symbol_map.byObject
for varid in vars_to_delete:
symbol = var_sm_byObject[varid]
del sm_byObject[varid]
del sm_bySymbol[symbol]
del variable_symbol_map
return symbol_map
def _print_model_LP(self,
model,
output_file,
solver_capability,
labeler,
output_fixed_variable_bounds=False,
file_determinism=1,
row_order=None,
column_order=None,
skip_trivial_constraints=False,
force_objective_constant=False,
include_all_variable_bounds=False):
symbol_map = SymbolMap()
variable_symbol_map = SymbolMap()
# NOTE: we use createSymbol instead of getSymbol because we
# know whether or not the symbol exists, and don't want
# to the overhead of error/duplicate checking.
# cache frequently called functions
create_symbol_func = SymbolMap.createSymbol
create_symbols_func = SymbolMap.createSymbols
alias_symbol_func = SymbolMap.alias
variable_label_pairs = []
# populate the symbol map in a single pass.
#objective_list, constraint_list, sosconstraint_list, variable_list \
# = self._populate_symbol_map(model,
# symbol_map,
# labeler,
# variable_symbol_map,
# file_determinism=file_determinism)
sortOrder = SortComponents.unsorted
if file_determinism >= 1:
sortOrder = sortOrder | SortComponents.indices
if file_determinism >= 2:
sortOrder = sortOrder | SortComponents.alphabetical
#
# Create variable symbols (and cache the block list)
#
all_blocks = []
variable_list = []
for block in model.block_data_objects(active=True,
sort=sortOrder):
all_blocks.append(block)
for vardata in block.component_data_objects(
Var,
active=True,
sort=sortOrder,
descend_into=False):
variable_list.append(vardata)
variable_label_pairs.append(
(vardata,create_symbol_func(symbol_map,
vardata,
labeler)))
variable_symbol_map.addSymbols(variable_label_pairs)
# and extract the information we'll need for rapid labeling.
object_symbol_dictionary = symbol_map.byObject
variable_symbol_dictionary = variable_symbol_map.byObject
# cache - these are called all the time.
print_expr_canonical = self._print_expr_canonical
# print the model name and the source, so we know roughly where
# it came from.
#
# NOTE: this *must* use the "\* ... *\" comment format: the GLPK
# LP parser does not correctly handle other formats (notably, "%").
output_file.write(
"\\* Source Pyomo model name=%s *\\\n\n" % (model.name,) )
#
# Objective
#
supports_quadratic_objective = \
solver_capability('quadratic_objective')
numObj = 0
onames = []
for block in all_blocks:
gen_obj_canonical_repn = \
getattr(block, "_gen_obj_canonical_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block,'_canonical_repn'):
block._canonical_repn = ComponentMap()
block_canonical_repn = block._canonical_repn
for objective_data in block.component_data_objects(
Objective,
active=True,
sort=sortOrder,
descend_into=False):
numObj += 1
onames.append(objective_data.name)
if numObj > 1:
raise ValueError(
"More than one active objective defined for input "
"model '%s'; Cannot write legal LP file\n"
"Objectives: %s" % (model.name, ' '.join(onames)))
create_symbol_func(symbol_map,
objective_data,
labeler)
symbol_map.alias(objective_data, '__default_objective__')
if objective_data.is_minimizing():
output_file.write("min \n")
else:
output_file.write("max \n")
if gen_obj_canonical_repn:
canonical_repn = \
generate_canonical_repn(objective_data.expr)
block_canonical_repn[objective_data] = canonical_repn
else:
canonical_repn = block_canonical_repn[objective_data]
degree = canonical_degree(canonical_repn)
if degree == 0:
logger.warning("Constant objective detected, replacing "
"with a placeholder to prevent solver failure.")
force_objective_constant = True
elif degree == 2:
if not supports_quadratic_objective:
raise RuntimeError(
"Selected solver is unable to handle "
"objective functions with quadratic terms. "
"Objective at issue: %s."
% objective_data.name)
elif degree != 1:
raise RuntimeError(
"Cannot write legal LP file. Objective '%s' "
"has nonlinear terms that are not quadratic."
% objective_data.name)
output_file.write(
object_symbol_dictionary[id(objective_data)]+':\n')
offset = print_expr_canonical(
canonical_repn,
output_file,
object_symbol_dictionary,
variable_symbol_dictionary,
True,
column_order,
force_objective_constant=force_objective_constant)
if numObj == 0:
raise ValueError(
"ERROR: No objectives defined for input model '%s'; "
" cannot write legal LP file" % str(model.name))
# Constraints
#
# If there are no non-trivial constraints, you'll end up with an empty
# constraint block. CPLEX is OK with this, but GLPK isn't. And
# eliminating the constraint block (i.e., the "s.t." line) causes GLPK
# to whine elsewhere. Output a warning if the constraint block is empty,
# so users can quickly determine the cause of the solve failure.
output_file.write("\n")
output_file.write("s.t.\n")
output_file.write("\n")
have_nontrivial = False
supports_quadratic_constraint = solver_capability('quadratic_constraint')
def constraint_generator():
for block in all_blocks:
gen_con_canonical_repn = \
getattr(block, "_gen_con_canonical_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block,'_canonical_repn'):
block._canonical_repn = ComponentMap()
block_canonical_repn = block._canonical_repn
for constraint_data in block.component_data_objects(
Constraint,
active=True,
sort=sortOrder,
descend_into=False):
if isinstance(constraint_data, LinearCanonicalRepn):
canonical_repn = constraint_data
else:
if gen_con_canonical_repn:
canonical_repn = generate_canonical_repn(constraint_data.body)
block_canonical_repn[constraint_data] = canonical_repn
else:
canonical_repn = block_canonical_repn[constraint_data]
yield constraint_data, canonical_repn
if row_order is not None:
sorted_constraint_list = list(constraint_generator())
sorted_constraint_list.sort(key=lambda x: row_order[x[0]])
def yield_all_constraints():
for constraint_data, canonical_repn in sorted_constraint_list:
yield constraint_data, canonical_repn
else:
yield_all_constraints = constraint_generator
# FIXME: This is a hack to get nested blocks working...
eq_string_template = "= %"+self._precision_string+'\n'
geq_string_template = ">= %"+self._precision_string+'\n\n'
leq_string_template = "<= %"+self._precision_string+'\n\n'
for constraint_data, canonical_repn in yield_all_constraints():
have_nontrivial = True
degree = canonical_degree(canonical_repn)
#
# Write constraint
#
# There are conditions, e.g., when fixing variables, under which
# a constraint block might be empty. Ignore these, for both
# practical reasons and the fact that the CPLEX LP format
# requires a variable in the constraint body. It is also
# possible that the body of the constraint consists of only a
# constant, in which case the "variable" of
if degree == 0:
if skip_trivial_constraints:
continue
elif degree == 2:
if not supports_quadratic_constraint:
raise ValueError(
"Solver unable to handle quadratic expressions. Constraint"
" at issue: '%s'" % (constraint_data.name))
elif degree != 1:
raise ValueError(
"Cannot write legal LP file. Constraint '%s' has a body "
"with nonlinear terms." % (constraint_data.name))
# Create symbol
con_symbol = create_symbol_func(symbol_map, constraint_data, labeler)
if constraint_data.equality:
label = 'c_e_' + con_symbol + '_'
alias_symbol_func(symbol_map, constraint_data, label)
output_file.write(label+':\n')
offset = print_expr_canonical(canonical_repn,
output_file,
object_symbol_dictionary,
variable_symbol_dictionary,
False,
column_order)
bound = constraint_data.lower
bound = self._get_bound(bound) - offset
output_file.write(eq_string_template
% (_no_negative_zero(bound)))
output_file.write("\n")
else:
if constraint_data.lower is not None:
if constraint_data.upper is not None:
label = 'r_l_' + con_symbol + '_'
else:
label = 'c_l_' + con_symbol + '_'
alias_symbol_func(symbol_map, constraint_data, label)
output_file.write(label+':\n')
offset = print_expr_canonical(canonical_repn,
output_file,
object_symbol_dictionary,
variable_symbol_dictionary,
False,
column_order)
bound = constraint_data.lower
bound = self._get_bound(bound) - offset
output_file.write(geq_string_template
% (_no_negative_zero(bound)))
if constraint_data.upper is not None:
if constraint_data.lower is not None:
label = 'r_u_' + con_symbol + '_'
else:
label = 'c_u_' + con_symbol + '_'
alias_symbol_func(symbol_map, constraint_data, label)
output_file.write(label+':\n')
offset = print_expr_canonical(canonical_repn,
output_file,
object_symbol_dictionary,
variable_symbol_dictionary,
False,
column_order)
bound = constraint_data.upper
bound = self._get_bound(bound) - offset
output_file.write(leq_string_template
% (_no_negative_zero(bound)))
if not have_nontrivial:
logger.warning('Empty constraint block written in LP format ' \
'- solver may error')
# the CPLEX LP format doesn't allow constants in the objective (or
# constraint body), which is a bit silly. To avoid painful
# book-keeping, we introduce the following "variable", constrained
# to the value 1. This is used when quadratic terms are present.
# worst-case, if not used, is that CPLEX easily pre-processes it out.
prefix = ""
output_file.write('%sc_e_ONE_VAR_CONSTANT: \n' % prefix)
output_file.write('%sONE_VAR_CONSTANT = 1.0\n' % prefix)
output_file.write("\n")
# SOS constraints
#
# For now, we write out SOS1 and SOS2 constraints in the cplex format
#
# All Component objects are stored in model._component, which is a
# dictionary of {class: {objName: object}}.
#
# Consider the variable X,
#
# model.X = Var(...)
#
# We print X to CPLEX format as X(i,j,k,...) where i, j, k, ... are the
# indices of X.
#
SOSlines = StringIO()
sos1 = solver_capability("sos1")
sos2 = solver_capability("sos2")
writtenSOS = False
for block in all_blocks:
for soscondata in block.component_data_objects(
SOSConstraint,
active=True,
sort=sortOrder,
descend_into=False):
create_symbol_func(symbol_map, soscondata, labeler)
level = soscondata.level
if (level == 1 and not sos1) or \
(level == 2 and not sos2) or \
(level > 2):
raise ValueError(
"Solver does not support SOS level %s constraints" % (level))
if writtenSOS == False:
SOSlines.write("SOS\n")
writtenSOS = True
# This updates the referenced_variable_ids, just in case
# there is a variable that only appears in an
# SOSConstraint, in which case this needs to be known
# before we write the "bounds" section (Cplex does not
# handle this correctly, Gurobi does)
self.printSOS(symbol_map,
labeler,
variable_symbol_map,
soscondata,
SOSlines)
#
# Bounds
#
output_file.write("bounds\n")
# Scan all variables even if we're only writing a subset of them.
# required because we don't store maps by variable type currently.
# FIXME: This is a hack to get nested blocks working...
lb_string_template = "%"+self._precision_string+" <= "
ub_string_template = " <= %"+self._precision_string+"\n"
# Track the number of integer and binary variables, so you can
# output their status later.
integer_vars = []
binary_vars = []
for vardata in variable_list:
# TODO: We could just loop over the set of items in
# self._referenced_variable_ids, except this is
# a dictionary that is hashed by id(vardata)
# which would make the bounds section
# nondeterministic (bad for unit testing)
if (not include_all_variable_bounds) and \
(id(vardata) not in self._referenced_variable_ids):
continue
if vardata.fixed:
if not 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 LP file." % (vardata.name, model.name))
if vardata.value is None:
raise ValueError("Variable cannot be fixed to a value of None.")
vardata_lb = value(vardata.value)
vardata_ub = value(vardata.value)
else:
vardata_lb = self._get_bound(vardata.lb)
vardata_ub = self._get_bound(vardata.ub)
name_to_output = variable_symbol_dictionary[id(vardata)]
# track the number of integer and binary variables, so we know whether
# to output the general / binary sections below.
if vardata.is_integer():
integer_vars.append(name_to_output)
elif vardata.is_binary():
binary_vars.append(name_to_output)
elif not vardata.is_continuous():
raise TypeError("Invalid domain type for variable with name '%s'. "
"Variable is not continuous, integer, or binary."
% (vardata.name))
# in the CPLEX LP file format, the default variable
# bounds are 0 and +inf. These bounds are in
# conflict with Pyomo, which assumes -inf and +inf
# (which we would argue is more rational).
output_file.write(" ")
if (vardata_lb is not None) and (vardata_lb != -infinity):
output_file.write(lb_string_template
% (_no_negative_zero(vardata_lb)))
else:
output_file.write(" -inf <= ")
if name_to_output == "e":
raise ValueError(
"Attempting to write variable with name 'e' in a CPLEX LP "
"formatted file will cause a parse failure due to confusion with "
"numeric values expressed in scientific notation")
output_file.write(name_to_output)
if (vardata_ub is not None) and (vardata_ub != infinity):
output_file.write(ub_string_template
% (_no_negative_zero(vardata_ub)))
else:
output_file.write(" <= +inf\n")
if len(integer_vars) > 0:
output_file.write("general\n")
for var_name in integer_vars:
output_file.write(' %s\n' % var_name)
if len(binary_vars) > 0:
output_file.write("binary\n")
for var_name in binary_vars:
output_file.write(' %s\n' % var_name)
# Write the SOS section
output_file.write(SOSlines.getvalue())
#
# wrap-up
#
output_file.write("end\n")
# Clean up the symbol map to only contain variables referenced
# in the active constraints **Note**: warm start method may
# rely on this for choosing the set of potential warm start
# variables
vars_to_delete = set(variable_symbol_map.byObject.keys()) - \
set(self._referenced_variable_ids.keys())
sm_byObject = symbol_map.byObject
sm_bySymbol = symbol_map.bySymbol
var_sm_byObject = variable_symbol_map.byObject
for varid in vars_to_delete:
symbol = var_sm_byObject[varid]
del sm_byObject[varid]
del sm_bySymbol[symbol]
del variable_symbol_map
return symbol_map
class CPLEXPersistent(CPLEXDirect, PersistentSolver):
"""The CPLEX LP/MIP solver
"""
pyomo.util.plugin.alias('_cplex_persistent',
doc='Persistent Python interface to the CPLEX LP/MIP solver')
def __init__(self, **kwds):
#
# Call base class constructor
#
kwds['type'] = 'cplexpersistent'
CPLEXDirect.__init__(self, **kwds)
# maps pyomo var data labels to the corresponding CPLEX variable id.
self._cplex_variable_ids = {}
self._cplex_variable_names = None
#
# updates all variable bounds in the compiled model - handles
# fixed variables and related issues. re-does everything from
# scratch by default, ignoring whatever was specified
# previously. if the value associated with the keyword
# vars_to_update is a non-empty list (assumed to be variable name
# / index pairs), then only the bounds for those variables are
# updated. this function assumes that the variables themselves
# already exist in the compiled model.
#
def compile_variable_bounds(self, pyomo_instance, vars_to_update):
from pyomo.core.base import Var
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot compile variable bounds - no "
"instance is presently compiled")
# the bound update entries should be name-value pairs
new_lower_bounds = []
new_upper_bounds = []
# operates through side effects on the above lists!
def update_bounds_lists(var_name):
var_lb = None
var_ub = None
if var_data.fixed and self._output_fixed_variable_bounds:
var_lb = var_ub = var_data.value
elif var_data.fixed:
# if we've been directed to not deal with fixed
# variables, then skip - they should have been
# compiled out of any description of the constraints
return
else:
if var_data.lb is None:
var_lb = -cplex.infinity
else:
var_lb = value(var_data.lb)
if var_data.ub is None:
var_ub = cplex.infinity
else:
var_ub= value(var_data.ub)
var_cplex_id = self._cplex_variable_ids[var_name]
new_lower_bounds.append((var_cplex_id, var_lb))
new_upper_bounds.append((var_cplex_id, var_ub))
if len(vars_to_update) == 0:
for var_data in pyomo_instance.component_data_objects(Var, active=True):
var_name = self._symbol_map.getSymbol(var_data, self._labeler)
update_bounds_lists(var_name)
else:
for var_name, var_index in vars_to_update:
var = pyomo_instance.find_component(var_name)
# TBD - do some error checking!
var_data = var[var_index]
var_name = self._symbol_map.getSymbol(var_data, self._labeler)
update_bounds_lists(var_name)
self._active_cplex_instance.variables.set_lower_bounds(new_lower_bounds)
self._active_cplex_instance.variables.set_upper_bounds(new_upper_bounds)
#
# method to compile objective of the input pyomo instance.
# TBD:
# it may be smarter just to track the associated pyomo instance,
# and re-compile it automatically from a cached local attribute.
# this would ensure consistency, among other things!
#
def compile_objective(self, pyomo_instance):
from pyomo.core.base import Objective
from pyomo.repn import canonical_is_constant, LinearCanonicalRepn, canonical_degree
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot compile objective - no "
"instance is presently compiled")
cplex_instance = self._active_cplex_instance
cntr = 0
for block in pyomo_instance.block_data_objects(active=True):
gen_obj_canonical_repn = \
getattr(block, "_gen_obj_canonical_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block,'_canonical_repn'):
block._canonical_repn = ComponentMap()
block_canonical_repn = block._canonical_repn
for obj_data in block.component_data_objects(Objective,
active=True,
descend_into=False):
cntr += 1
if cntr > 1:
raise ValueError(
"Multiple active objectives found on Pyomo instance '%s'. "
"Solver '%s' will only handle a single active objective" \
% (pyomo_instance.cname(True), self.type))
if obj_data.is_minimizing():
cplex_instance.objective.set_sense(
cplex_instance.objective.sense.minimize)
else:
cplex_instance.objective.set_sense(
cplex_instance.objective.sense.maximize)
cplex_instance.objective.set_name(
self._symbol_map.getSymbol(obj_data,
self._labeler))
if gen_obj_canonical_repn:
obj_repn = generate_canonical_repn(obj_data.expr)
block_canonical_repn[obj_data] = obj_repn
else:
obj_repn = block_canonical_repn[obj_data]
if (isinstance(obj_repn, LinearCanonicalRepn) and \
(obj_repn.linear == None)) or \
canonical_is_constant(obj_repn):
print("Warning: Constant objective detected, replacing "
"with a placeholder to prevent solver failure.")
offset = obj_repn.constant
if offset is None:
offset = 0.0
objective_expression = [("ONE_VAR_CONSTANT",offset)]
cplex_instance.objective.set_linear(objective_expression)
else:
if isinstance(obj_repn, LinearCanonicalRepn):
objective_expression, offset = \
self._encode_constraint_body_linear_specialized(
obj_repn,
self._labeler,
use_variable_names=False,
cplex_variable_name_index_map=self._cplex_variable_ids,
as_pairs=True)
if offset != 0.0:
objective_expression.append((self._cplex_variable_ids["ONE_VAR_CONSTANT"],offset))
cplex_instance.objective.set_linear(objective_expression)
else:
#Linear terms
if 1 in obj_repn:
objective_expression, offset = \
self._encode_constraint_body_linear(
obj_repn,
self._labeler,
as_pairs=True)
if offset != 0.0:
objective_expression.append(("ONE_VAR_CONSTANT",offset))
cplex_instance.objective.set_linear(objective_expression)
#Quadratic terms
if 2 in obj_repn:
self._has_quadratic_objective = True
objective_expression = \
self._encode_constraint_body_quadratic(obj_repn,
self._labeler,
as_triples=True,
is_obj=2.0)
cplex_instance.objective.\
set_quadratic_coefficients(objective_expression)
degree = canonical_degree(obj_repn)
if (degree is None) or (degree > 2):
raise ValueError(
"CPLEXPersistent plugin does not support general nonlinear "
"objective expressions (only linear or quadratic).\n"
"Objective: %s" % (obj_data.cname(True)))
#
# method to populate the CPLEX problem instance (interface) from
# the supplied Pyomo problem instance.
#
def compile_instance(self,
pyomo_instance,
symbolic_solver_labels=False,
output_fixed_variable_bounds=False,
skip_trivial_constraints=False):
from pyomo.core.base import Var, Constraint, SOSConstraint
from pyomo.repn import canonical_is_constant, LinearCanonicalRepn, canonical_degree
self._symbolic_solver_labels = symbolic_solver_labels
self._output_fixed_variable_bounds = output_fixed_variable_bounds
self._skip_trivial_constraints = skip_trivial_constraints
self._has_quadratic_constraints = False
self._has_quadratic_objective = False
used_sos_constraints = False
self._active_cplex_instance = cplex.Cplex()
if self._symbolic_solver_labels:
labeler = self._labeler = TextLabeler()
else:
labeler = self._labeler = NumericLabeler('x')
self._symbol_map = SymbolMap()
self._instance = pyomo_instance
pyomo_instance.solutions.add_symbol_map(self._symbol_map)
self._smap_id = id(self._symbol_map)
# we use this when iterating over the constraints because it
# will have a much smaller hash table, we also use this for
# the warm start code after it is cleaned to only contain
# variables referenced in the constraints
self._variable_symbol_map = SymbolMap()
# cplex wants the caller to set the problem type, which is (for
# current purposes) strictly based on variable type counts.
num_binary_variables = 0
num_integer_variables = 0
num_continuous_variables = 0
#############################################
# populate the variables in the cplex model #
#############################################
var_names = []
var_lbs = []
var_ubs = []
var_types = []
self._referenced_variable_ids.clear()
# maps pyomo var data labels to the corresponding CPLEX variable id.
self._cplex_variable_ids.clear()
# cached in the loop below - used to update the symbol map
# immediately following loop termination.
var_label_pairs = []
for var_data in pyomo_instance.component_data_objects(Var, active=True):
if var_data.fixed and not self._output_fixed_variable_bounds:
# if a variable is fixed, and we're preprocessing
# fixed variables (as in not outputting them), there
# is no need to add them to the compiled model.
continue
var_name = self._symbol_map.getSymbol(var_data, labeler)
var_names.append(var_name)
var_label_pairs.append((var_data, var_name))
self._cplex_variable_ids[var_name] = len(self._cplex_variable_ids)
if (var_data.lb is None) or (var_data.lb == -infinity):
var_lbs.append(-cplex.infinity)
else:
var_lbs.append(value(var_data.lb))
if (var_data.ub is None) or (var_data.ub == infinity):
var_ubs.append(cplex.infinity)
else:
var_ubs.append(value(var_data.ub))
if var_data.is_integer():
var_types.append(self._active_cplex_instance.variables.type.integer)
num_integer_variables += 1
elif var_data.is_binary():
var_types.append(self._active_cplex_instance.variables.type.binary)
num_binary_variables += 1
elif var_data.is_continuous():
var_types.append(self._active_cplex_instance.variables.type.continuous)
num_continuous_variables += 1
else:
raise TypeError("Invalid domain type for variable with name '%s'. "
"Variable is not continuous, integer, or binary.")
self._active_cplex_instance.variables.add(names=var_names,
lb=var_lbs,
ub=var_ubs,
types=var_types)
self._active_cplex_instance.variables.add(lb=[1],
ub=[1],
names=["ONE_VAR_CONSTANT"])
self._cplex_variable_ids["ONE_VAR_CONSTANT"] = len(self._cplex_variable_ids)
self._variable_symbol_map.addSymbols(var_label_pairs)
self._cplex_variable_names = self._active_cplex_instance.variables.get_names()
########################################################
# populate the standard constraints in the cplex model #
########################################################
expressions = []
senses = []
rhss = []
range_values = []
names = []
qexpressions = []
qlinears = []
qsenses = []
qrhss = []
qnames = []
for block in pyomo_instance.block_data_objects(active=True):
gen_con_canonical_repn = \
getattr(block, "_gen_con_canonical_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block,'_canonical_repn'):
block._canonical_repn = ComponentMap()
block_canonical_repn = block._canonical_repn
for con in block.component_data_objects(Constraint,
active=True,
descend_into=False):
if (con.lower is None) and \
(con.upper is None):
continue # not binding at all, don't bother
con_repn = None
if isinstance(con, LinearCanonicalRepn):
con_repn = con
else:
if gen_con_canonical_repn:
con_repn = generate_canonical_repn(con.body)
block_canonical_repn[con] = con_repn
else:
con_repn = block_canonical_repn[con]
# There are conditions, e.g., when fixing variables, under which
# a constraint block might be empty. Ignore these, for both
# practical reasons and the fact that the CPLEX LP format
# requires a variable in the constraint body. It is also
# possible that the body of the constraint consists of only a
# constant, in which case the "variable" of
if isinstance(con_repn, LinearCanonicalRepn):
if (con_repn.linear is None) and \
self._skip_trivial_constraints:
continue
else:
# we shouldn't come across a constant canonical repn
# that is not LinearCanonicalRepn
assert not canonical_is_constant(con_repn)
name = self._symbol_map.getSymbol(con, labeler)
expr = None
qexpr = None
quadratic = False
if isinstance(con_repn, LinearCanonicalRepn):
expr, offset = \
self._encode_constraint_body_linear_specialized(con_repn,
labeler,
use_variable_names=False,
cplex_variable_name_index_map=self._cplex_variable_ids)
else:
degree = canonical_degree(con_repn)
if degree == 2:
quadratic = True
elif (degree != 0) or (degree != 1):
raise ValueError(
"CPLEXPersistent plugin does not support general nonlinear "
"constraint expression (only linear or quadratic).\n"
"Constraint: %s" % (con.cname(True)))
expr, offset = self._encode_constraint_body_linear(con_repn,
labeler)
if quadratic:
if expr is None:
expr = cplex.SparsePair(ind=[0],val=[0.0])
self._has_quadratic_constraints = True
qexpr = self._encode_constraint_body_quadratic(con_repn,labeler)
qnames.append(name)
if con.equality:
# equality constraint.
qsenses.append('E')
qrhss.append(self._get_bound(con.lower) - offset)
elif (con.lower is not None) and (con.upper is not None):
raise RuntimeError(
"The CPLEXDirect plugin can not translate range "
"constraints containing quadratic expressions.")
elif con.lower is not None:
assert con.upper is None
qsenses.append('G')
qrhss.append(self._get_bound(con.lower) - offset)
else:
qsenses.append('L')
qrhss.append(self._get_bound(con.upper) - offset)
qlinears.append(expr)
qexpressions.append(qexpr)
else:
names.append(name)
expressions.append(expr)
if con.equality:
# equality constraint.
senses.append('E')
rhss.append(self._get_bound(con.lower) - offset)
range_values.append(0.0)
elif (con.lower is not None) and (con.upper is not None):
# ranged constraint.
senses.append('R')
lower_bound = self._get_bound(con.lower) - offset
upper_bound = self._get_bound(con.upper) - offset
rhss.append(lower_bound)
range_values.append(upper_bound - lower_bound)
elif con.lower is not None:
senses.append('G')
rhss.append(self._get_bound(con.lower) - offset)
range_values.append(0.0)
else:
senses.append('L')
rhss.append(self._get_bound(con.upper) - offset)
range_values.append(0.0)
###################################################
# populate the SOS constraints in the cplex model #
###################################################
# SOS constraints - largely taken from cpxlp.py so updates there,
# should be applied here
# TODO: Allow users to specify the variables coefficients for custom
# branching/set orders - refer to cpxlp.py
sosn = self._capabilities.sosn
sos1 = self._capabilities.sos1
sos2 = self._capabilities.sos2
modelSOS = ModelSOS()
for soscondata in pyomo_instance.component_data_objects(SOSConstraint,
active=True):
level = soscondata.level
if (level == 1 and not sos1) or \
(level == 2 and not sos2) or \
(level > 2 and not sosn):
raise Exception("Solver does not support SOS level %s constraints"
% (level,))
modelSOS.count_constraint(self._symbol_map,
labeler,
self._variable_symbol_map,
soscondata)
if modelSOS.sosType:
for key in modelSOS.sosType:
self._active_cplex_instance.SOS.add(type = modelSOS.sosType[key],
name = modelSOS.sosName[key],
SOS = [modelSOS.varnames[key],
modelSOS.weights[key]])
self._referenced_variable_ids.update(modelSOS.varids[key])
used_sos_constraints = True
self._active_cplex_instance.linear_constraints.add(
lin_expr=expressions,
senses=senses,
rhs=rhss,
range_values=range_values,
names=names)
for index in xrange(len(qexpressions)):
self._active_cplex_instance.quadratic_constraints.add(
lin_expr=qlinears[index],
quad_expr=qexpressions[index],
sense=qsenses[index],
rhs=qrhss[index],
name=qnames[index])
#############################################
# populate the objective in the cplex model #
#############################################
self.compile_objective(pyomo_instance)
################################################
# populate the problem type in the cplex model #
################################################
# This gets rid of the annoying "Freeing MIP data." message.
def _filter_freeing_mip_data(val):
if val.strip() == 'Freeing MIP data.':
return ""
return val
self._active_cplex_instance.set_warning_stream(sys.stderr,
fn=_filter_freeing_mip_data)
if (self._has_quadratic_objective is True) or \
(self._has_quadratic_constraints is True):
if (num_integer_variables > 0) or \
(num_binary_variables > 0) or \
(used_sos_constraints):
if self._has_quadratic_constraints is True:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.MIQCP)
else:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.MIQP)
else:
if self._has_quadratic_constraints is True:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.QCP)
else:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.QP)
elif (num_integer_variables > 0) or \
(num_binary_variables > 0) or \
(used_sos_constraints):
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.MILP)
else:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.LP)
# restore the warning stream without our filter function
self._active_cplex_instance.set_warning_stream(sys.stderr)
#
# simple method to query whether a Pyomo instance has already been
# compiled.
#
def instance_compiled(self):
return self._active_cplex_instance is not None
#
# Override base class method to check for compiled instance
#
def _warm_start(self, instance):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot warm start - no instance is "
"presently compiled")
# clear any existing warm starts.
self._active_cplex_instance.MIP_starts.delete()
# the iteration order is identical to that used in generating
# the cplex instance, so all should be well.
variable_ids = []
variable_values = []
# IMPT: the var_data returned is a weak ref!
for label, var_data in iteritems(self._variable_symbol_map.bySymbol):
cplex_id = self._cplex_variable_ids[label]
if var_data().fixed and not self._output_fixed_variable_bounds:
continue
elif var_data().value is not None:
variable_ids.append(cplex_id)
variable_values.append(var_data().value)
if len(variable_ids):
self._active_cplex_instance.MIP_starts.add(
[variable_ids, variable_values],
self._active_cplex_instance.MIP_starts.effort_level.auto)
#
# Override base class method to check for compiled instance
#
def _populate_cplex_instance(self, model):
assert model == self._instance
def _presolve(self, *args, **kwds):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin"
" cannot presolve - no instance is "
"presently compiled")
# These must be passed in to the compile_instance method,
# but assert that any values here match those already supplied
if 'symbolic_solver_labels' in kwds:
assert self._symbolic_solver_labels == \
kwds['symbolic_solver_labels']
if 'output_fixed_variable_bounds' in kwds:
assert self._output_fixed_variable_bounds == \
kwds['output_fixed_variable_bounds']
if 'skip_trivial_constraints' in kwds:
assert self._skip_trivial_constraints == \
kwds["skip_trivial_constraints"]
if self._smap_id not in self._instance.solutions.symbol_map:
self._instance.solutions.add_symbol_map(self._symbol_map)
CPLEXDirect._presolve(self, *args, **kwds)
# like other solver plugins, persistent solver plugins can
# take an instance as an input argument. the only context in
# which this instance is used, however, is for warm-starting.
if len(args) > 2:
raise ValueError("The CPLEXPersistent plugin method "
"'_presolve' can be supplied at most "
"one problem instance - %s were "
"supplied" % len(args))
# Re-add the symbol map id if it was cleared
# after a previous solution load
if id(self._symbol_map) not in args[0].solutions.symbol_map:
args[0].solutions.add_symbol_map(self._symbol_map)
self._smap_id = id(self._symbol_map)
#
# invoke the solver on the currently compiled instance!!!
#
def _apply_solver(self):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin cannot "
"apply solver - no instance is presently compiled")
# NOTE:
# CPLEX maintains the pool of feasible solutions from the
# prior solve as the set of mip starts for the next solve.
# and evaluating multiple mip starts (and there can be many)
# is expensive. so if the warm_start method is not invoked,
# there will potentially be a lot of time wasted.
return CPLEXDirect._apply_solver(self)
def _postsolve(self):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot postsolve - no instance is "
"presently compiled")
active_cplex_instance = self._active_cplex_instance
variable_symbol_map = self._variable_symbol_map
instance = self._instance
ret = CPLEXDirect._postsolve(self)
#
# These get reset to None by the base class method
#
self._active_cplex_instance = active_cplex_instance
self._variable_symbol_map = variable_symbol_map
self._instance = instance
return ret
