def _initialize_problem(self):
self.problem = glp_create_prob()
glp_create_index(self.problem)
glp_scale_prob(self.problem, GLP_SF_AUTO)
if self.name is not None:
_glpk_validate_id(self.name)
glp_set_prob_name(self.problem, str(self.name))
def test_swiglpk(self): """Test the underlying GLPK lib and its SWIG interface based on the example from https://github.com/biosustain/swiglpk """ ia = glp.intArray(1 + 1000) ja = glp.intArray(1 + 1000) ar = glp.doubleArray(1 + 1000) lp = glp.glp_create_prob() smcp = glp.glp_smcp() glp.glp_init_smcp(smcp) smcp.msg_lev = glp.GLP_MSG_ALL # use GLP_MSG_ERR? glp.glp_set_prob_name(lp, "sample") glp.glp_set_obj_dir(lp, glp.GLP_MAX) glp.glp_add_rows(lp, 3) glp.glp_set_row_name(lp, 1, "p") glp.glp_set_row_bnds(lp, 1, glp.GLP_UP, 0.0, 100.0) glp.glp_set_row_name(lp, 2, "q") glp.glp_set_row_bnds(lp, 2, glp.GLP_UP, 0.0, 600.0) glp.glp_set_row_name(lp, 3, "r") glp.glp_set_row_bnds(lp, 3, glp.GLP_UP, 0.0, 300.0) glp.glp_add_cols(lp, 3) glp.glp_set_col_name(lp, 1, "x1") glp.glp_set_col_bnds(lp, 1, glp.GLP_LO, 0.0, 0.0) glp.glp_set_obj_coef(lp, 1, 10.0) glp.glp_set_col_name(lp, 2, "x2") glp.glp_set_col_bnds(lp, 2, glp.GLP_LO, 0.0, 0.0) glp.glp_set_obj_coef(lp, 2, 6.0) glp.glp_set_col_name(lp, 3, "x3") glp.glp_set_col_bnds(lp, 3, glp.GLP_LO, 0.0, 0.0) glp.glp_set_obj_coef(lp, 3, 4.0) ia[1] = 1; ja[1] = 1; ar[1] = 1.0 # a[1,1] = 1 ia[2] = 1; ja[2] = 2; ar[2] = 1.0 # a[1,2] = 1 ia[3] = 1; ja[3] = 3; ar[3] = 1.0 # a[1,3] = 1 ia[4] = 2; ja[4] = 1; ar[4] = 10.0 # a[2,1] = 10 ia[5] = 3; ja[5] = 1; ar[5] = 2.0 # a[3,1] = 2 ia[6] = 2; ja[6] = 2; ar[6] = 4.0 # a[2,2] = 4 ia[7] = 3; ja[7] = 2; ar[7] = 2.0 # a[3,2] = 2 ia[8] = 2; ja[8] = 3; ar[8] = 5.0 # a[2,3] = 5 ia[9] = 3; ja[9] = 3; ar[9] = 6.0 # a[3,3] = 6 glp.glp_load_matrix(lp, 9, ia, ja, ar) glp.glp_simplex(lp, smcp) Z = glp.glp_get_obj_val(lp) x1 = glp.glp_get_col_prim(lp, 1) x2 = glp.glp_get_col_prim(lp, 2) x3 = glp.glp_get_col_prim(lp, 3) self.assertAlmostEqual(Z, 733.3333, 4) self.assertAlmostEqual(x1, 33.3333, 4) self.assertAlmostEqual(x2, 66.6667, 4) self.assertAlmostEqual(x3, 0) glp.glp_delete_prob(lp)
def setup_linprog(
recipes: Dict[str, 'Recipe'],
min_rates: Optional[Dict[str, float]] = None,
min_clocks: Optional[Dict[str, int]] = None,
fixed_clocks: Optional[Dict[str, int]] = None,
) -> SwigPyObject:
if min_rates is None:
min_rates = {}
if min_clocks is None:
min_clocks = {}
if fixed_clocks is None:
fixed_clocks = {}
resources = sorted({p for r in recipes.values() for p in r.rates.keys()})
resource_indices: Dict[str,
int] = {r: i
for i, r in enumerate(resources, 1)}
m = len(resources)
n = len(recipes)
problem: SwigPyObject = lp.glp_create_prob()
lp.glp_set_prob_name(problem, 'satisfactory')
lp.glp_set_obj_name(problem, 'percentage_sum')
lp.glp_set_obj_dir(problem, lp.GLP_MIN)
lp.glp_add_rows(problem, m)
lp.glp_add_cols(problem, n)
for i, resource in enumerate(resources, 1):
setup_row(problem, i, resource, min_rates.get(resource, 0))
for j, recipe in enumerate(recipes.values(), 1):
setup_col(
problem,
j,
recipe,
resource_indices,
min_clocks.get(recipe.name),
fixed_clocks.get(recipe.name),
)
lp.glp_create_index(problem)
return problem
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)
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)
