def _get_primal_values(self):
n = glp_get_num_cols(self.problem)
if glp_get_num_int(self.problem) == 0:
return [glp_get_col_prim(self.problem, i + 1) for i in range(n)]
else:
return [glp_mip_col_val(self.problem, i + 1) for i in range(n)]
def _add_constraints(self, constraints, sloppy=False):
super(Model, self)._add_constraints(constraints, sloppy=sloppy)
for constraint in constraints:
constraint._problem = None # This needs to be done in order to not trigger constraint._get_expression()
glp_add_rows(self.problem, 1)
index = glp_get_num_rows(self.problem)
glp_set_row_name(self.problem, index,
str(constraint.name).encode())
num_cols = glp_get_num_cols(self.problem)
index_array = ffi.new("int[{}]".format(num_cols +
1)) #intArray(num_cols + 1)
value_array = ffi.new(
"double[{}]".format(num_cols + 1)) #doubleArray(num_cols + 1)
num_vars = 0 # constraint.variables is too expensive for large problems
offset, coef_dict, _ = parse_optimization_expression(constraint,
linear=True)
num_vars = len(coef_dict)
for i, (var, coef) in enumerate(coef_dict.items()):
index_array[i + 1] = var._index
value_array[i + 1] = float(coef)
glp_set_mat_row(self.problem, index, num_vars, index_array,
value_array)
constraint._problem = self
self._glpk_set_row_bounds(constraint)
def _add_variables(self, variables):
for variable in variables:
glp_add_cols(self.problem, 1)
index = glp_get_num_cols(self.problem)
glp_set_col_name(self.problem, index, str(variable.name).encode())
variable.problem = self
self._glpk_set_col_bounds(variable)
glp_set_col_kind(self.problem, variable._index,
_VTYPE_TO_GLPK_VTYPE[variable.type])
super(Model, self)._add_variables(variables)
def get_linear_coefficients(self, variables):
if self.problem is not None:
num_cols = glp_get_num_cols(self.problem.problem)
ia = ffi.new("int[{}]".format(num_cols + 1))
da = ffi.new("double[{}]".format(num_cols + 1))
nnz = glp_get_mat_row(self.problem.problem, self._index, ia, da)
coefs = dict.fromkeys(variables, 0.0)
coefs.update({
self.problem._variables[ia[i + 1] - 1]: da[i + 1]
for i in range(nnz)
if self.problem._variables[ia[i + 1] - 1] in variables
})
return coefs
else:
raise Exception(
"Can't get coefficients from solver if constraint is not in a model"
)
def _get_expression(self):
if self.problem is not None:
col_num = glp_get_num_cols(self.problem.problem)
ia = ffi.new("int[{}]".format(col_num +
1)) # intArray(col_num + 1)
da = ffi.new("double[{}]".format(col_num +
1)) # doubleArray(col_num + 1)
nnz = glp_get_mat_row(self.problem.problem, self._index, ia, da)
constraint_variables = [
self.problem._variables[glp_get_col_name(
self.problem.problem, ia[i])] for i in range(1, nnz + 1)
]
expression = symbolics.add([
symbolics.mul(
(symbolics.Real(da[i]), constraint_variables[i - 1]))
for i in range(1, nnz + 1)
])
self._expression = expression
return self._expression
def set_linear_coefficients(self, coefficients):
if self.problem is not None:
problem = self.problem.problem
num_cols = glp_get_num_cols(problem)
ia = ffi.new("int[{}]".format(col_num + 1))
va = ffi.new("double[{}]".format(col_num + 1))
num_rows = glp_get_mat_row(self.problem.problem, self._index, ia,
va)
variables_and_coefficients = {
var.name: coeff
for var, coeff in six.iteritems(coefficients)
}
final_variables_and_coefficients = {
glp_get_col_name(problem, ia[i]): va[i]
for i in range(1, num_rows + 1)
}
final_variables_and_coefficients.update(variables_and_coefficients)
ia = ffi.new("int[{}]".format(col_num + 1))
va = ffi.new("double[{}]".format(col_num + 1))
for i, (name, coeff) in enumerate(
six.iteritems(final_variables_and_coefficients)):
ia[i + 1] = self.problem._variables[name]._index
va[i + 1] = float(coeff)
glp_set_mat_row(problem, self._index,
len(final_variables_and_coefficients), ia, va)
else:
raise Exception(
"Can't change coefficients if constraint is not associated with a model."
)
def _get_reduced_costs(self):
if self.is_integer:
raise ValueError(
"Dual values are not well-defined for integer problems")
n = glp_get_num_cols(self.problem)
return [glp_get_col_dual(self.problem, i + 1) for i in range(n)]
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).encode())
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 = ffi.new("int[{}]".format(col_num + 1))
da = ffi.new("double[{}]".format(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 term_generator():
for index in range(1,
glp_get_num_cols(self.problem.problem) + 1):
coeff = glp_get_obj_coef(self.problem.problem, index)
if coeff != 0.:
yield (symbolics.Real(coeff), variables[index - 1])