def _initialize_model_from_problem(self,
problem,
vc_mapping=None,
offset=0):
if not isinstance(problem, OSQPProblem):
raise TypeError("Provided problem is not a valid OSQP model.")
self.problem = problem
for name in self.problem.variables:
var = Variable(
name,
lb=self.problem.variable_lbs[name],
ub=self.problem.variable_ubs[name],
problem=self,
)
super(Model, self)._add_variables([var])
for name in self.problem.constraints:
# Since constraint expressions are lazily retrieved from the
# solver they don't have to be built here
lhs = symbolics.Integer(0)
constr = Constraint(
lhs,
lb=self.problem.constraint_lbs[name],
ub=self.problem.constraint_ubs[name],
name=name,
problem=self,
)
super(Model, self)._add_constraints([constr], sloppy=True)
if vc_mapping is None:
for constr in self.constraints:
name = constr.name
for variable in constr.variables:
try:
self._variables_to_constraints_mapping[
variable.name].add(name)
except KeyError:
self._variables_to_constraints_mapping[
variable.name] = set([name])
else:
self._variables_to_constraints_mapping = vc_mapping
linear_expression = add([
coef * self._variables[vn]
for vn, coef in six.iteritems(self.problem.obj_linear_coefs)
])
quadratic_expression = add([
coef * self._variables[vn[0]] * self._variables[vn[1]]
for vn, coef in six.iteritems(self.problem.obj_quadratic_coefs)
])
self._objective_offset = offset
self._objective = Objective(
linear_expression + quadratic_expression + offset,
problem=self,
direction={
-1: "max",
1: "min"
}[self.problem.direction],
name="osqp_objective",
)
def _initialize_model_from_problem(self, problem):
try:
self.problem = problem
glp_create_index(self.problem)
except TypeError:
raise TypeError("Provided problem is not a valid GLPK model.")
row_num = glp_get_num_rows(self.problem)
col_num = glp_get_num_cols(self.problem)
for i in range(1, col_num + 1):
var = Variable(
glp_get_col_name(self.problem, i),
lb=glp_get_col_lb(self.problem, i),
ub=glp_get_col_ub(self.problem, i),
problem=self,
type=_GLPK_VTYPE_TO_VTYPE[
glp_get_col_kind(self.problem, i)]
)
# This avoids adding the variable to the glpk problem
super(Model, self)._add_variables([var])
variables = self.variables
for j in range(1, row_num + 1):
ia = intArray(col_num + 1)
da = doubleArray(col_num + 1)
nnz = glp_get_mat_row(self.problem, j, ia, da)
constraint_variables = [variables[ia[i] - 1] for i in range(1, nnz + 1)]
# Since constraint expressions are lazily retrieved from the solver they don't have to be built here
# lhs = _unevaluated_Add(*[da[i] * constraint_variables[i - 1]
# for i in range(1, nnz + 1)])
lhs = 0
glpk_row_type = glp_get_row_type(self.problem, j)
if glpk_row_type == GLP_FX:
row_lb = glp_get_row_lb(self.problem, j)
row_ub = row_lb
elif glpk_row_type == GLP_LO:
row_lb = glp_get_row_lb(self.problem, j)
row_ub = None
elif glpk_row_type == GLP_UP:
row_lb = None
row_ub = glp_get_row_ub(self.problem, j)
elif glpk_row_type == GLP_DB:
row_lb = glp_get_row_lb(self.problem, j)
row_ub = glp_get_row_ub(self.problem, j)
elif glpk_row_type == GLP_FR:
row_lb = None
row_ub = None
else:
raise Exception(
"Currently, optlang does not support glpk row type %s"
% str(glpk_row_type)
)
log.exception()
if isinstance(lhs, int):
lhs = symbolics.Integer(lhs)
elif isinstance(lhs, float):
lhs = symbolics.Real(lhs)
constraint_id = glp_get_row_name(self.problem, j)
for variable in constraint_variables:
try:
self._variables_to_constraints_mapping[variable.name].add(constraint_id)
except KeyError:
self._variables_to_constraints_mapping[variable.name] = set([constraint_id])
super(Model, self)._add_constraints(
[Constraint(lhs, lb=row_lb, ub=row_ub, name=constraint_id, problem=self, sloppy=True)],
sloppy=True
)
term_generator = (
(glp_get_obj_coef(self.problem, index), variables[index - 1])
for index in range(1, glp_get_num_cols(problem) + 1)
)
self._objective = Objective(
symbolics.add(
[symbolics.mul((symbolics.Real(term[0]), term[1])) for term in term_generator if
term[0] != 0.]
),
problem=self,
direction={GLP_MIN: 'min', GLP_MAX: 'max'}[glp_get_obj_dir(self.problem)])
glp_scale_prob(self.problem, GLP_SF_AUTO)
def __init__(self, problem=None, *args, **kwargs):
super(Model, self).__init__(*args, **kwargs)
if problem is None:
self.problem = cplex.Cplex()
elif isinstance(problem, cplex.Cplex):
self.problem = problem
zipped_var_args = zip(
self.problem.variables.get_names(),
self.problem.variables.get_lower_bounds(),
self.problem.variables.get_upper_bounds(),
# self.problem.variables.get_types(), # TODO uncomment when cplex is fixed
)
for name, lb, ub in zipped_var_args:
var = Variable(name, lb=lb, ub=ub,
problem=self) # Type should also be in there
super(Model, self)._add_variables([
var
]) # This avoids adding the variable to the glpk problem
zipped_constr_args = zip(
self.problem.linear_constraints.get_names(),
self.problem.linear_constraints.get_rows(),
self.problem.linear_constraints.get_senses(),
self.problem.linear_constraints.get_rhs())
variables = self._variables
for name, row, sense, rhs in zipped_constr_args:
constraint_variables = [variables[i - 1] for i in row.ind]
# Since constraint expressions are lazily retrieved from the solver they don't have to be built here
# lhs = _unevaluated_Add(*[val * variables[i - 1] for i, val in zip(row.ind, row.val)])
lhs = symbolics.Integer(0)
if sense == 'E':
constr = Constraint(lhs,
lb=rhs,
ub=rhs,
name=name,
problem=self)
elif sense == 'G':
constr = Constraint(lhs, lb=rhs, name=name, problem=self)
elif sense == 'L':
constr = Constraint(lhs, ub=rhs, name=name, problem=self)
elif sense == 'R':
range_val = self.problem.linear_constraints.get_range_values(
name)
if range_val > 0:
constr = Constraint(lhs,
lb=rhs,
ub=rhs + range_val,
name=name,
problem=self)
else:
constr = Constraint(lhs,
lb=rhs + range_val,
ub=rhs,
name=name,
problem=self)
else: # pragma: no cover
raise Exception(
'%s is not a recognized constraint sense.' % sense)
for variable in constraint_variables:
try:
self._variables_to_constraints_mapping[
variable.name].add(name)
except KeyError:
self._variables_to_constraints_mapping[
variable.name] = set([name])
super(Model, self)._add_constraints([constr], sloppy=True)
try:
objective_name = self.problem.objective.get_name()
except CplexSolverError as e:
if 'CPLEX Error 1219:' not in str(e):
raise e
else:
linear_expression = add([
mul(symbolics.Real(coeff), variables[index]) for index,
coeff in enumerate(self.problem.objective.get_linear())
if coeff != 0.
])
try:
quadratic = self.problem.objective.get_quadratic()
except IndexError:
quadratic_expression = Zero
else:
quadratic_expression = self._get_quadratic_expression(
quadratic)
self._objective = Objective(
linear_expression + quadratic_expression,
problem=self,
direction={
self.problem.objective.sense.minimize: 'min',
self.problem.objective.sense.maximize: 'max'
}[self.problem.objective.get_sense()],
name=objective_name)
else:
raise TypeError("Provided problem is not a valid CPLEX model.")
self.configuration = Configuration(problem=self, verbosity=0)
