def _optimize(self):
status = self._run_glp_simplex()
if status == interface.UNDEFINED and self.configuration.presolve is True:
# If presolve is on, status will be undefined if not optimal
self.configuration.presolve = False
status = self._run_glp_simplex()
self.configuration.presolve = True
if (glp_get_num_int(self.problem) + glp_get_num_bin(self.problem)) > 0:
status = self._run_glp_mip()
if status == 'undefined' or status == 'infeasible':
# Let's see if the presolver and some scaling can fix this issue
glp_scale_prob(self.problem, GLP_SF_AUTO)
original_presolve_setting = self.configuration.presolve
self.configuration.presolve = True
status = self._run_glp_mip()
self.configuration.presolve = original_presolve_setting
self._status = status
return status
def _optimize(self):
status = self._run_glp_simplex()
# Sometimes GLPK gets itself stuck with an invalid basis. This will help it get rid of it.
if status == interface.UNDEFINED and self.configuration.presolve is not True:
glp_adv_basis(self.problem, 0)
status = self._run_glp_simplex()
if status == interface.UNDEFINED and self.configuration.presolve is True:
# If presolve is on, status will be undefined if not optimal
self.configuration.presolve = False
status = self._run_glp_simplex()
self.configuration.presolve = True
if self._glpk_is_mip():
status = self._run_glp_mip()
if status == 'undefined' or status == 'infeasible':
# Let's see if the presolver and some scaling can fix this issue
glp_scale_prob(self.problem, GLP_SF_AUTO)
original_presolve_setting = self.configuration.presolve
self.configuration.presolve = True
status = self._run_glp_mip()
self.configuration.presolve = original_presolve_setting
return status
def _optimize(self):
status = self._run_glp_simplex()
# Sometimes GLPK gets itself stuck with an invalid basis. This will help it get rid of it.
if status == interface.UNDEFINED and self.configuration.presolve is not True:
glp_adv_basis(self.problem, 0)
status = self._run_glp_simplex()
if status == interface.UNDEFINED and self.configuration.presolve is True:
# If presolve is on, status will be undefined if not optimal
self.configuration.presolve = False
status = self._run_glp_simplex()
self.configuration.presolve = True
if (glp_get_num_int(self.problem) + glp_get_num_bin(self.problem)) > 0:
status = self._run_glp_mip()
if status == 'undefined' or status == 'infeasible':
# Let's see if the presolver and some scaling can fix this issue
glp_scale_prob(self.problem, GLP_SF_AUTO)
original_presolve_setting = self.configuration.presolve
self.configuration.presolve = True
status = self._run_glp_mip()
self.configuration.presolve = original_presolve_setting
return status
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)
self.configuration = Configuration()
if problem is None:
self.problem = glp_create_prob()
glp_create_index(self.problem)
if self.name is not None:
glp_set_prob_name(self.problem, str(self.name))
else:
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 = sympy.Integer(lhs)
elif isinstance(lhs, float):
lhs = sympy.RealNumber(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(
_unevaluated_Add(
*[_unevaluated_Mul(sympy.RealNumber(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)
