def get_col_bounds(self):
'get column bounds'
lp_cols = self.get_num_cols()
# column lower and upper bounds
col_bounds = []
for col in range(lp_cols):
col_type = glpk.glp_get_col_type(self.lp, col + 1)
ub = np.inf
lb = -np.inf
if col_type == glpk.GLP_DB:
ub = glpk.glp_get_col_ub(self.lp, col + 1)
lb = glpk.glp_get_col_lb(self.lp, col + 1)
elif col_type == glpk.GLP_LO:
lb = glpk.glp_get_col_lb(self.lp, col + 1)
elif col_type == glpk.GLP_FX:
lb = ub = glpk.glp_get_col_lb(self.lp, col + 1)
else:
assert col_type == glpk.GLP_FR, "unsupported col type in get_col_bounds()"
col_bounds.append((lb, ub))
return col_bounds
def test_change_variable_bounds(self):
inner_prob = self.model.problem
inner_problem_bounds = [(glp_get_col_lb(inner_prob, i), glp_get_col_ub(inner_prob, i)) for i in
range(1, glp_get_num_cols(inner_prob) + 1)]
bounds = [(var.lb, var.ub) for var in self.model.variables.values()]
self.assertEqual(bounds, inner_problem_bounds)
for var in self.model.variables.values():
var.lb = random.uniform(-1000, 1000)
var.ub = random.uniform(var.lb, 1000)
inner_problem_bounds_new = [(glp_get_col_lb(inner_prob, i), glp_get_col_ub(inner_prob, i)) for i in
range(1, glp_get_num_cols(inner_prob) + 1)]
bounds_new = [(var.lb, var.ub) for var in self.model.variables.values()]
self.assertNotEqual(bounds, bounds_new)
self.assertNotEqual(inner_problem_bounds, inner_problem_bounds_new)
self.assertEqual(bounds_new, inner_problem_bounds_new)
def test_glpk_setting_bounds(self):
self.model.add(self.var)
self.model.update()
var = self.var
model = self.model
var.lb = 1
self.assertEqual(var.lb, 1)
self.assertEqual(glp_get_col_lb(model.problem, var._index), 1)
var.ub = 2
self.assertEqual(var.ub, 2)
self.assertEqual(glp_get_col_ub(model.problem, var._index), 2)
def test_glpk_setting_bounds(self):
self.model.add(self.var)
self.model.update()
var = self.var
model = self.model
var.lb = 1
self.assertEqual(var.lb, 1)
self.assertEqual(glp_get_col_lb(model.problem, var._index), 1)
var.ub = 2
self.assertEqual(var.ub, 2)
self.assertEqual(glp_get_col_ub(model.problem, var._index), 2)
def test_change_variable_bounds(self):
inner_prob = self.model.problem
inner_problem_bounds = [
(glp_get_col_lb(inner_prob, i), glp_get_col_ub(inner_prob, i))
for i in range(1,
glp_get_num_cols(inner_prob) + 1)
]
bounds = [(var.lb, var.ub) for var in self.model.variables.values()]
self.assertEqual(bounds, inner_problem_bounds)
for var in self.model.variables.values():
var.lb = random.uniform(-1000, 1000)
var.ub = random.uniform(var.lb, 1000)
inner_problem_bounds_new = [
(glp_get_col_lb(inner_prob, i), glp_get_col_ub(inner_prob, i))
for i in range(1,
glp_get_num_cols(inner_prob) + 1)
]
bounds_new = [(var.lb, var.ub)
for var in self.model.variables.values()]
self.assertNotEqual(bounds, bounds_new)
self.assertNotEqual(inner_problem_bounds, inner_problem_bounds_new)
self.assertEqual(bounds_new, inner_problem_bounds_new)
def _column_names_str(self):
'get the line in __str__ for the column names'
rv = " "
dbl_max = sys.float_info.max
for col, name in enumerate(self.names):
name = self.names[col]
lb = glpk.glp_get_col_lb(self.lp, col + 1)
ub = glpk.glp_get_col_ub(self.lp, col + 1)
if lb != -dbl_max or ub != dbl_max:
name = "*" + name
name = name.rjust(6)[:6] # fix width to exactly 6
rv += name + " "
rv += "\n"
return rv
def _var_bounds_str(self):
'get the variable bounds string used in __str__'
rv = ""
dbl_max = sys.float_info.max
added_label = False
for index, name in enumerate(self.names):
lb = glpk.glp_get_col_lb(self.lp, index + 1)
ub = glpk.glp_get_col_ub(self.lp, index + 1)
if not added_label and (lb != -dbl_max or ub != dbl_max):
added_label = True
rv += "(*) Bounded variables:"
if lb != -dbl_max or ub != dbl_max:
lb = "-inf" if lb == -dbl_max else lb
ub = "inf" if ub == dbl_max else ub
rv += f"\n{name} in [{lb}, {ub}]"
return rv
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)
