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 _get_primal(self):
if self.type == "continuous":
primal_from_solver = glp_get_col_prim(self.problem.problem, self.index)
elif self.type in ("binary", "integer"):
primal_from_solver = glp_mip_col_val(self.problem.problem, self.index)
else:
raise TypeError("Unknown variable type")
return primal_from_solver
def _get_primal(self):
if self.problem._glpk_is_mip():
primal_from_solver = glp_mip_col_val(self.problem.problem,
self._index)
else:
primal_from_solver = glp_get_col_prim(self.problem.problem,
self._index)
return primal_from_solver
def _process_simplex_result(self, simplex_res, columns):
'''process the result of a glp_simplex call
returns None on UNSAT, otherwise the optimization result with the requested columns
if columns is None, will return full result
'''
rv = None
if simplex_res == glpk.GLP_ENOPFS: # no primal feasible w/ presolver
rv = None
elif simplex_res != 0: # simplex failed, report the error
raise RuntimeError("glp_simplex returned nonzero status ({}): {}".format(
simplex_res, LpInstance.get_simplex_error_string(simplex_res)))
else:
status = glpk.glp_get_status(self.lp)
if status == glpk.GLP_NOFEAS: # infeasible
rv = None
elif status == glpk.GLP_OPT: # optimal
lp_cols = self.get_num_cols()
if columns is None:
rv = np.zeros(lp_cols)
else:
rv = np.zeros(len(columns))
# copy the output vars
rv_len = len(rv)
for i in range(rv_len):
col = i if columns is None else columns[i]
assert 0 <= col < lp_cols, "out of bounds column requested in LP solution: {}".format(col)
rv[i] = glpk.glp_get_col_prim(self.lp, int(col + 1))
else: # neither infeasible nor optimal (for example, unbounded)
codes = [glpk.GLP_OPT, glpk.GLP_FEAS, glpk.GLP_INFEAS, glpk.GLP_NOFEAS, glpk.GLP_UNBND, glpk.GLP_UNDEF]
msgs = ["solution is optimal",
"solution is feasible",
"solution is infeasible",
"problem has no feasible solution",
"problem has unbounded solution",
"solution is undefined"]
if status == glpk.GLP_UNBND:
ray = glpk.glp_get_unbnd_ray(self.lp)
raise RuntimeError(f"LP had unbounded solution in minimize(). Unbounded ray was variable #{ray}")
for code, message in zip(codes, msgs):
if status == code:
raise RuntimeError("LP status after solving in minimize() was '{}': {}".format(message, code))
raise RuntimeError("LP status after solving in minimize() was <Unknown>: {}".format(status))
return rv
def getFlowRates(self, flows):
if isinstance(flows, basestring):
flows = (flows, )
self._solve()
return np.array([
glp.glp_get_col_prim(self._lp, 1 + self._flows[flow])
if flow in self._flows else None for flow in flows
])
def _get_primal(self):
if self.type == "continuous":
primal_from_solver = glp_get_col_prim(self.problem.problem,
self._index)
elif self.type in ("binary", "integer"):
primal_from_solver = glp_mip_col_val(self.problem.problem,
self._index)
else:
raise AssertionError("Unknown variable type")
return primal_from_solver
def _process_simplex_result(self, simplex_res):
'''process the result of a glp_simplex call
returns None on UNSAT, otherwise the optimization result with the requested columns
if columns is None, will return full result
'''
rv = None
if simplex_res != glpk.GLP_ENOPFS: # skip if no primal feasible w/ presolver
if simplex_res != 0: # simplex failed, report the error
raise RuntimeError(
"glp_simplex returned nonzero status ({}): {}".format(
simplex_res,
LpInstance.get_simplex_error_string(simplex_res)))
status = glpk.glp_get_status(self.lp)
if status == glpk.GLP_NOFEAS: # infeasible
rv = None
elif status == glpk.GLP_OPT: # optimal
lp_cols = self.get_num_cols()
rv = np.zeros(lp_cols)
for col in range(lp_cols):
rv[col] = glpk.glp_get_col_prim(self.lp, int(1 + col))
else: # neither infeasible nor optimal (for example, unbounded)
error_msg = "<Unknown Status>"
codes = [
glpk.GLP_OPT, glpk.GLP_FEAS, glpk.GLP_INFEAS,
glpk.GLP_NOFEAS, glpk.GLP_UNBND, glpk.GLP_UNDEF
]
msgs = [
"solution is optimal", "solution is feasible",
"solution is infeasible",
"problem has no feasible solution",
"problem has unbounded solution", "solution is undefined"
]
for code, message in zip(codes, msgs):
if status == code:
error_msg = message
break
if status == glpk.GLP_UNBND:
ray = glpk.glp_get_unbnd_ray(self.lp)
error_msg += f"; unbounded ray was variable #{ray}"
raise RuntimeError(
f"LP status after minimize() was {status}: {error_msg}")
return rv
def primal_values(self):
primal_values = collections.OrderedDict()
is_mip = self._glpk_is_mip()
for index, variable in enumerate(self.variables):
if is_mip:
value = glp_mip_col_val(self.problem, index + 1)
else:
value = glp_get_col_prim(self.problem, index + 1)
primal_values[variable.name] = variable._round_primal_to_bounds(
value)
return primal_values
def primal_values(self):
primal_values = collections.OrderedDict()
for index, variable in enumerate(self.variables):
if variable.type == "continuous":
value = glp_get_col_prim(self.problem, index + 1)
elif variable.type in ["binary", "integer"]:
value = glp_mip_col_val(self.problem, index + 1)
else:
raise AssertionError("Unknown variable type")
primal_values[variable.name] = variable._round_primal_to_bounds(value)
return primal_values
def primal_values(self):
primal_values = collections.OrderedDict()
for index, variable in enumerate(self.variables):
if variable.type == "continuous":
value = glp_get_col_prim(self.problem, index + 1)
elif variable.type in ["binary", "integer"]:
value = glp_mip_col_val(self.problem, index + 1)
else:
raise AssertionError("Unknown variable type")
primal_values[variable.name] = variable._round_primal_to_bounds(
value)
return primal_values
def _linprog(c, A, b, obj):
lp = glpk.glp_create_prob()
glpk.glp_set_obj_dir(lp, obj)
params = glpk.glp_smcp()
glpk.glp_init_smcp(params)
params.msg_lev = glpk.GLP_MSG_OFF #Only print error messages from GLPK
num_rows = A.shape[0]
num_cols = A.shape[1]
mat_size = num_rows * num_cols
glpk.glp_add_rows(lp, num_rows)
for row_ind in range(num_rows):
glpk.glp_set_row_bnds(lp, row_ind + 1, glpk.GLP_UP, 0.0,
float(b[row_ind]))
glpk.glp_add_cols(lp, num_cols)
for col_ind in range(num_cols):
glpk.glp_set_col_bnds(lp, col_ind + 1, glpk.GLP_FR, 0.0, 0.0)
glpk.glp_set_obj_coef(lp, col_ind + 1, c[col_ind])
'Swig arrays are used for feeding constraints in GLPK'
ia, ja, ar = [], [], []
for i, j in product(range(num_rows), range(num_cols)):
ia.append(i + 1)
ja.append(j + 1)
ar.append(float(A[i][j]))
ia = glpk.as_intArray(ia)
ja = glpk.as_intArray(ja)
ar = glpk.as_doubleArray(ar)
glpk.glp_load_matrix(lp, mat_size, ia, ja, ar)
glpk.glp_simplex(lp, params)
fun = glpk.glp_get_obj_val(lp)
x = [
i for i in map(lambda x: glpk.glp_get_col_prim(lp, x + 1),
range(num_cols))
]
glpk.glp_delete_prob(lp)
glpk.glp_free_env()
return LPSolution(x, fun)
def _get_value(self, variable):
"""Return value of variable in solution."""
return swiglpk.glp_get_col_prim(self._problem._p,
self._problem._variables[variable])
def _solve(self):
import swiglpk as glpk
# An alias to the internal problem instance.
p = self.int
continuous = self.ext.is_continuous()
# Select LP solver (Simplex or Interior Point Method).
if continuous:
if self.ext.options["lp_root_method"] == "interior":
interior = True
else:
# Default to Simplex.
interior = False
simplex = not interior
else:
simplex = interior = False
# Select appropriate options container.
if simplex:
options = glpk.glp_smcp()
glpk.glp_init_smcp(options)
elif interior:
options = glpk.glp_iptcp()
glpk.glp_init_iptcp(options)
else:
options = glpk.glp_iocp()
glpk.glp_init_iocp(options)
# Handle "verbose" option.
verbosity = self.verbosity()
if verbosity < 0:
options.msg_lev = glpk.GLP_MSG_OFF
elif verbosity == 0:
options.msg_lev = glpk.GLP_MSG_ERR
elif verbosity == 1:
options.msg_lev = glpk.GLP_MSG_ON
elif verbosity >= 2:
options.msg_lev = glpk.GLP_MSG_ALL
# Handle "tol" option.
# Note that GLPK knows three different tolerances for Simplex but none
# for the Interior Point Method, while PICOS states that "tol" is meant
# only for the IPM.
# XXX: The option is unsupported but does not default to None, so we
# cannot warn the user.
pass
# Handle "maxit" option.
if not simplex:
self._handle_unsupported_option("maxit",
"GLPK supports the 'maxit' option only with Simplex.")
elif self.ext.options["maxit"] is not None:
options.it_lim = int(self.ext.options["maxit"])
# Handle "lp_root_method" option.
# Note that the PICOS option is explicitly also meant for the MIP
# preprocessing step but GLPK does not support it in that scenario.
if not continuous:
self._handle_unsupported_option("lp_root_method",
"GLPK supports the 'lp_root_method' option only for LPs.")
elif self.ext.options["lp_root_method"] is not None:
if self.ext.options["lp_root_method"] == "interior":
# Handled above.
pass
elif self.ext.options["lp_root_method"] == "psimplex":
assert simplex
options.meth = glpk.GLP_PRIMAL
elif self.ext.options["lp_root_method"] == "dsimplex":
assert simplex
options.meth = glpk.GLP_DUAL
else:
self._handle_bad_option_value("lp_root_method")
# Handle "timelimit" option.
if interior:
self._handle_unsupported_option("timelimit",
"GLPK does not support the 'timelimit' option with the "
"Interior Point Method.")
elif self.ext.options["timelimit"] is not None:
options.tm_lim = 1000 * int(self.ext.options["timelimit"])
# Handle "gaplim" option.
# Note that the option is silently ignored if passed alongside an LP;
# while the solver does not allow us to pass the option in that case, it
# is still technically a valid option as every LP is also a MIP.
# TODO: Find out if "mip_gap" is really equivalent to "gaplim".
if self.ext.options["gaplim"] is not None:
if not continuous:
options.mip_gap = float(self.ext.options["gaplim"])
# Handle unsupported options.
self._handle_unsupported_options(
"lp_node_method", "treememory", "nbsol", "hotstart")
# TODO: Add GLPK-sepcific options. Candidates are:
# For both Simplex and MIPs:
# tol_*, out_*
# For Simplex:
# pricing, r_test, obj_*
# For the Interior Point Method:
# ord_alg
# For MIPs:
# *_tech, *_heur, ps_tm_lim, *_cuts, cb_size, binarize
# Attempt to solve the problem.
with self._header():
with self._stopwatch():
if simplex:
# TODO: Support glp_exact.
error = glpk.glp_simplex(p, options)
elif interior:
error = glpk.glp_interior(p, options)
else:
options.presolve = glpk.GLP_ON
error = glpk.glp_intopt(p, options)
# Conert error codes to text output.
# Note that by printing it above the footer, this output is made to
# look like it's coming from GLPK, which is technically wrong but
# semantically correct.
if error == glpk.GLP_EBADB:
self._warn("Unable to start the search, because the initial "
"basis specified in the problem object is invalid.")
elif error == glpk.GLP_ESING:
self._warn("Unable to start the search, because the basis "
"matrix corresponding to the initial basis is singular "
"within the working precision.")
elif error == glpk.GLP_ECOND:
self._warn("Unable to start the search, because the basis "
"matrix corresponding to the initial basis is "
"ill-conditioned.")
elif error == glpk.GLP_EBOUND:
self._warn("Unable to start the search, because some double-"
"bounded variables have incorrect bounds.")
elif error == glpk.GLP_EFAIL:
self._warn("The search was prematurely terminated due to a "
"solver failure.")
elif error == glpk.GLP_EOBJLL:
self._warn("The search was prematurely terminated, because the "
"objective function being maximized has reached its lower "
"limit and continues decreasing.")
elif error == glpk.GLP_EOBJUL:
self._warn("The search was prematurely terminated, because the "
"objective function being minimized has reached its upper "
"limit and continues increasing.")
elif error == glpk.GLP_EITLIM:
self._warn("The search was prematurely terminated, because the "
"simplex iteration limit has been exceeded.")
elif error == glpk.GLP_ETMLIM:
self._warn("The search was prematurely terminated, because the "
"time limit has been exceeded.")
elif error == glpk.GLP_ENOPFS:
self._verbose("The LP has no primal feasible solution.")
elif error == glpk.GLP_ENODFS:
self._verbose("The LP has no dual feasible solution.")
elif error != 0:
self._warn("GLPK error {:d}.".format(error))
# Retrieve primals.
if self.ext.options["noprimals"]:
primals = None
else:
primals = {}
for variable in self.ext.variables.values():
value = []
for localIndex, picosIndex \
in enumerate(range(variable.startIndex, variable.endIndex)):
glpkIndex = self._picos2glpk_variable_index(picosIndex)
if simplex:
localValue = glpk.glp_get_col_prim(p, glpkIndex);
elif interior:
localValue = glpk.glp_ipt_col_prim(p, glpkIndex);
else:
localValue = glpk.glp_mip_col_val(p, glpkIndex);
value.append(localValue)
primals[variable.name] = value
# Retrieve duals.
# XXX: Returns the duals as a flat cvx.matrix to be consistent with
# other solvers. This feels incorrect when the constraint was given
# as a proper two dimensional matrix.
if self.ext.options["noduals"] or not continuous:
duals = None
else:
duals = []
rowOffset = 1
for constraintNum, constraint in enumerate(self.ext.constraints):
assert isinstance(constraint, AffineConstraint)
numRows = len(constraint)
values = []
for localConIndex in range(numRows):
glpkConIndex = rowOffset + localConIndex
if simplex:
localValue = glpk.glp_get_row_dual(p, glpkConIndex);
elif interior:
localValue = glpk.glp_ipt_row_dual(p, glpkConIndex);
else:
assert False
values.append(localValue)
if constraint.is_decreasing():
duals.append(-cvxopt.matrix(values))
else:
duals.append(cvxopt.matrix(values))
rowOffset += numRows
if glpk.glp_get_obj_dir(p) == glpk.GLP_MIN:
duals = [-d for d in duals]
# Retrieve objective value.
if simplex:
objectiveValue = glpk.glp_get_obj_val(p)
elif interior:
objectiveValue = glpk.glp_ipt_obj_val(p)
else:
objectiveValue = glpk.glp_mip_obj_val(p)
# Retrieve solution metadata.
meta = {}
if simplex:
# Set common entry "status".
status = glpk.glp_get_status(p)
if status is glpk.GLP_OPT:
meta["status"] = "optimal"
elif status is glpk.GLP_FEAS:
meta["status"] = "feasible"
elif status in (glpk.GLP_INFEAS, glpk.GLP_NOFEAS):
meta["status"] = "infeasible"
elif status is glpk.GLP_UNBND:
meta["status"] = "unbounded"
elif status is glpk.GLP_UNDEF:
meta["status"] = "undefined"
else:
meta["status"] = "unknown"
# Set GLPK-specific entry "primal_status".
primalStatus = glpk.glp_get_prim_stat(p)
if primalStatus is glpk.GLP_FEAS:
meta["primal_status"] = "feasible"
elif primalStatus in (glpk.GLP_INFEAS, glpk.GLP_NOFEAS):
meta["primal_status"] = "infeasible"
elif primalStatus is glpk.GLP_UNDEF:
meta["primal_status"] = "undefined"
else:
meta["primal_status"] = "unknown"
# Set GLPK-specific entry "dual_status".
dualStatus = glpk.glp_get_dual_stat(p)
if dualStatus is glpk.GLP_FEAS:
meta["dual_status"] = "feasible"
elif dualStatus in (glpk.GLP_INFEAS, glpk.GLP_NOFEAS):
meta["dual_status"] = "infeasible"
elif dualStatus is glpk.GLP_UNDEF:
meta["dual_status"] = "undefined"
else:
meta["dual_status"] = "unknown"
elif interior:
# Set common entry "status".
status = glpk.glp_ipt_status(p)
if status is glpk.GLP_OPT:
meta["status"] = "optimal"
elif status in (glpk.GLP_INFEAS, glpk.GLP_NOFEAS):
meta["status"] = "infeasible"
elif status is glpk.GLP_UNDEF:
meta["status"] = "undefined"
else:
meta["status"] = "unknown"
else:
# Set common entry "status".
status = glpk.glp_mip_status(p)
if status is glpk.GLP_OPT:
meta["status"] = "optimal"
elif status is glpk.GLP_FEAS:
meta["status"] = "feasible"
elif status is glpk.GLP_NOFEAS:
meta["status"] = "infeasible"
elif status is glpk.GLP_UNDEF:
meta["status"] = "undefined"
else:
meta["status"] = "unknown"
return (primals, duals, objectiveValue, meta)
def _get_var_value(self, variable):
self._check_valid()
if variable not in self._problem._variables:
raise ValueError('Unknown variable: {}'.format(variable))
return swiglpk.glp_get_col_prim(self._problem._p,
self._problem._variables[variable])
def _get_primal(self):
if self.problem._glpk_is_mip():
primal_from_solver = glp_mip_col_val(self.problem.problem, self._index)
else:
primal_from_solver = glp_get_col_prim(self.problem.problem, self._index)
return primal_from_solver
def columnPrimalValue(self, j):
"""Return the primal value of the structural variable for j-th column."""
return glp.glp_get_col_prim(self._lp, j)
def _get_var_value(self, variable):
self._check_valid()
if variable not in self._problem._variables:
raise ValueError('Unknown variable: {}'.format(variable))
return swiglpk.glp_get_col_prim(
self._problem._p, self._problem._variables[variable])