def QPModel(self, addW=False):
A = self.A
c = self.c
s = CyClpSimplex()
x = s.addVariable('x', self.nCols)
if addW:
w = s.addVariable('w', self.nCols)
s += A * x >= 1
n = self.nCols
if not addW:
s += 0 <= x <= 1
else:
s += x + w == 1
s += 0 <= w <= 1
## s += -1 <= x <= 1
s.objective = c * x
if addW:
G = sparse.lil_matrix((2*n, 2*n))
for i in xrange(n/2, n): #xrange(n-1):
G[i, i] = 1
G[2*n-1, 2*n-1] = 10**-10
else:
G = sparse.lil_matrix((n, n))
for i in xrange(n/2, n): #xrange(n-1):
G[i, i] = 1
s.Hessian = G
return s
def test2(self):
'Same as test1, but use cylp indirectly.'
s = CyClpSimplex()
x = s.addVariable('x', 3)
A = np.matrix([[1,2,3], [1,1,1]])
b = CyLPArray([5, 3])
s += A * x == b
s += x >= 0
s.objective = 1 * x[0] + 1 * x[1] + 1.1 * x[2]
# Solve it a first time
s.primal()
sol = s.primalVariableSolution['x']
self.assertTrue((abs(sol - np.array([1,2,0]) ) <= 10**-6).all())
# Add a cut
s.addConstraint(x[0] >= 1.1)
s.primal()
sol = s.primalVariableSolution['x']
self.assertTrue((abs(sol - np.array([1.1, 1.8, 0.1]) ) <= 10**-6).all())
# Change the objective function
c = csr_matrixPlus([[1, 10, 1.1]]).T
s.objective = c.T * x
s.primal()
sol = s.primalVariableSolution['x']
self.assertTrue((abs(sol - np.array([2, 0, 1]) ) <= 10**-6).all())
def read_instance(module_name = None, file_name = None):
if module_name is not None:
lp = CyClpSimplex()
mip = ilib.import_module(module_name)
A = np.matrix(mip.A)
#print np.linalg.cond(A)
b = CyLPArray(mip.b)
#Warning: At the moment, you must put bound constraints in explicitly for split cuts
x_l = CyLPArray([0 for _ in range(mip.numVars)])
x = lp.addVariable('x', mip.numVars)
lp += x >= x_l
try:
x_u = CyLPArray(getattr(mip, 'x_u'))
lp += x <= x_u
except:
pass
lp += (A * x <= b if mip.sense[1] == '<=' else
A * x >= b)
c = CyLPArray(mip.c)
lp.objective = -c * x if mip.sense[0] == 'Max' else c * x
return lp, x, mip.A, mip.b, mip.sense[1], mip.integerIndices
elif file_name is not None:
lp = CyClpSimplex()
m = lp.extractCyLPModel(file_name)
x = m.getVarByName('x')
integerIndices = [i for (i, j) in enumerate(lp.integerInformation) if j == True]
infinity = lp.getCoinInfinity()
sense = None
for i in range(lp.nRows):
if lp.constraintsLower[i] > -infinity:
if sense == '<=':
print "Function does not support mixed constraint..."
break
else:
sense = '>='
b = lp.constraintsLower
if lp.constraintsUpper[i] < infinity:
if sense == '>=':
print "Function does not support mixed constraint..."
break
else:
sense = '<='
b = lp.constraintsUpper
return lp, x, lp.coefMatrix, b, sense, integerIndices
else:
print "No file or module name specified..."
return None, None, None, None, None, None
def model(self):
A = self.A
c = self.c
s = CyClpSimplex()
x = s.addVariable('x', self.nCols)
s += A * x >= 1
s += 0 <= x <= 1
s.objective = c * x
return s
def test_removeVar2(self):
s = CyClpSimplex()
fp = os.path.join(currentFilePath, '../../input/p0033.mps')
s.extractCyLPModel(fp)
y = s.addVariable('y', 3)
s.primal()
x = s.getVarByName('x')
s.addConstraint(x[1] + y[1] >= 1.2)
#s.primal()
s.removeVariable('x')
s.primal()
s = s.primalVariableSolution
self.assertTrue((s['y'] - np.array([0, 1.2, 0]) <= 10**-6).all())
def test_multiDim(self):
from cylp.cy import CyClpSimplex
from cylp.py.modeling.CyLPModel import CyLPArray
s = CyClpSimplex()
x = s.addVariable('x', (5, 3, 6))
s += 2 * x[2, :, 3].sum() + 3 * x[0, 1, :].sum() >= 5
s += 0 <= x <= 1
c = CyLPArray(range(18))
s.objective = c * x[2, :, :] + c * x[0, :, :]
s.primal()
sol = s.primalVariableSolution['x']
self.assertTrue(abs(sol[0, 1, 0] - 1) <= 10**-6)
self.assertTrue(abs(sol[2, 0, 3] - 1) <= 10**-6)
def test_ArrayIndexing(self):
from cylp.cy import CyClpSimplex
from cylp.py.modeling.CyLPModel import CyLPArray
s = CyClpSimplex()
x = s.addVariable('x', (5, 3, 6))
s += 2 * x[2, :, 3].sum() + 3 * x[0, 1, :].sum() >= 5
s += x[1, 2, [0, 3, 5]] - x[2, 1, np.array([1, 2, 4])] == 1
s += 0 <= x <= 1
c = CyLPArray(range(18))
s.objective = c * x[2, :, :] + c * x[0, :, :]
s.primal()
sol = s.primalVariableSolution['x']
self.assertTrue(abs(sol[1, 2, 0] - 1) <= 10**-6)
self.assertTrue(abs(sol[1, 2, 3] - 1) <= 10**-6)
self.assertTrue(abs(sol[1, 2, 5] - 1) <= 10**-6)
def test_onlyBounds2(self):
s = CyClpSimplex()
x = s.addVariable('x', 3)
y = s.addVariable('y', 2)
s += y >= 1
s += 2 <= x <= 4
c = CyLPArray([1., -2., 3.])
s.objective = c * x + 2 * y[0] + 2 * y[1]
s.primal()
sol = np.concatenate((s.primalVariableSolution['x'],
s.primalVariableSolution['y']))
self.assertTrue((abs(sol -
np.array([2, 4, 2, 1, 1]) ) <= 10**-6).all())
def test_multiDim_Cbc_solve(self):
from cylp.cy import CyClpSimplex
from cylp.py.modeling.CyLPModel import CyLPArray
s = CyClpSimplex()
x = s.addVariable('x', (5, 3, 6))
s += 2 * x[2, :, 3].sum() + 3 * x[0, 1, :].sum() >= 5.5
s += 0 <= x <= 2.2
c = CyLPArray(range(18))
s.objective = c * x[2, :, :] + c * x[0, :, :]
s.setInteger(x)
cbcModel = s.getCbcModel()
cbcModel.solve()
sol_x = cbcModel.primalVariableSolution['x']
self.assertTrue(abs(sol_x[0, 1, 0] - 1) <= 10**-6)
self.assertTrue(abs(sol_x[2, 0, 3] - 2) <= 10**-6)
def read_instance(module_name = True, file_name = None):
if module_name:
lp = CyClpSimplex()
mip = ilib.import_module(module_name)
A = np.matrix(mip.A)
#print np.linalg.cond(A)
b = CyLPArray(mip.b)
#We assume variables have zero lower bounds
x_l = CyLPArray([0 for _ in range(mip.numVars)])
x = lp.addVariable('x', mip.numVars)
lp += x >= x_l
try:
x_u = CyLPArray(getattr(mip, 'x_u'))
lp += x <= x_u
except:
pass
lp += (A * x <= b if mip.sense[1] == '<=' else
A * x >= b)
c = CyLPArray(mip.c)
lp.objective = -c * x if mip.sense[0] == 'Max' else c * x
return lp, x, mip.A, mip.b, mip.sense, mip.integerIndices
else:
#TODO Change sense of inequalities so they are all the same
# by explicitly checking lp.constraintsUpper and lp.constraintsLower
#Warning: Reading MP not well tested
lp.extractCyLPModel(file_name)
x = lp.cyLPModel.getVarByName('x')
sense = ('Min', '>=')
return lp, x, None, None, sense, integerIndices
#if isinstance(key, tuple):
if val == 0: # See if necessary to use a tolerance
return
self.sol[key] = val
def __repr__(self):
return repr(self.sol)
def getCoinInfinity():
return 1.79769313486e+308
if __name__ == '__main__':
from cylp.cy import CyClpSimplex
from cylp.py.modeling.CyLPModel import CyLPArray
s = CyClpSimplex()
x = s.addVariable('x', (5, 3, 6))
s += 2 * x[2, :, 3].sum() + 3 * x[0, 1, :].sum() >= 5
s += 0 <= x <= 1
c = CyLPArray(range(18))
s.objective = c * x[2, :, :] + c * x[0, :, :]
s.writeMps('/Users/mehdi/Desktop/test.mps')
s.primal()
sol = s.primalVariableSolution
print sol
#model = CyLPModel()
#
#x = model.addVariable('x', 5)
#y = model.addVariable('y', 4)
def solve(self, objective, constraints, cached_data,
warm_start, verbose, solver_opts):
"""Returns the result of the call to the solver.
Parameters
----------
objective : LinOp
The canonicalized objective.
constraints : list
The list of canonicalized cosntraints.
cached_data : dict
A map of solver name to cached problem data.
warm_start : bool
Not used.
verbose : bool
Should the solver print output?
solver_opts : dict
Additional arguments for the solver.
Returns
-------
tuple
(status, optimal value, primal, equality dual, inequality dual)
"""
# Import basic modelling tools of cylp
from cylp.cy import CyClpSimplex
from cylp.py.modeling.CyLPModel import CyLPArray
# Get problem data
data = self.get_problem_data(objective, constraints, cached_data)
c = data[s.C]
b = data[s.B]
A = data[s.A]
dims = data[s.DIMS]
n = c.shape[0]
solver_cache = cached_data[self.name()]
# Problem
model = CyClpSimplex()
# Variables
x = model.addVariable('x', n)
if self.is_mip(data):
for i in data[s.BOOL_IDX]:
model.setInteger(x[i])
for i in data[s.INT_IDX]:
model.setInteger(x[i])
# Constraints
# eq
model += A[0:dims[s.EQ_DIM], :] * x == b[0:dims[s.EQ_DIM]]
# leq
leq_start = dims[s.EQ_DIM]
leq_end = dims[s.EQ_DIM] + dims[s.LEQ_DIM]
model += A[leq_start:leq_end, :] * x <= b[leq_start:leq_end]
# no boolean vars available in cbc -> model as int + restrict to [0,1]
if self.is_mip(data):
for i in data[s.BOOL_IDX]:
model += 0 <= x[i] <= 1
# Objective
model.objective = c
# Build model & solve
status = None
if self.is_mip(data):
cbcModel = model.getCbcModel() # need to convert model
if not verbose:
cbcModel.logLevel = 0
# Add cut-generators (optional)
for cut_name, cut_func in six.iteritems(self.SUPPORTED_CUT_GENERATORS):
if cut_name in solver_opts and solver_opts[cut_name]:
module = importlib.import_module("cylp.cy.CyCgl")
funcToCall = getattr(module, cut_func)
cut_gen = funcToCall()
cbcModel.addCutGenerator(cut_gen, name=cut_name)
# solve
status = cbcModel.branchAndBound()
else:
if not verbose:
model.logLevel = 0
status = model.primal() # solve
results_dict = {}
results_dict["status"] = status
if self.is_mip(data):
results_dict["x"] = cbcModel.primalVariableSolution['x']
results_dict["obj_value"] = cbcModel.objectiveValue
else:
results_dict["x"] = model.primalVariableSolution['x']
results_dict["obj_value"] = model.objectiveValue
return self.format_results(results_dict, data, cached_data)
if p is not None:
if CYLP_INSTALLED:
lp = CyClpSimplex()
A = np.matrix(p.hrep.A)
b = CyLPArray(p.hrep.b)
print(A)
print(b)
if LP.numVars == 2:
disp_polyhedron(A = A, b = b)
x = lp.addVariable('x', LP.numVars)
if LP.sense[0] == '>=':
lp += A * x >= b
else:
lp += A * x <= b
#lp += x >= 0
c = CyLPArray(LP.c)
# We are maximizing, so negate objective
if LP.sense[1] == 'Min':
lp.objective = c * x
else:
lp.objective = -c * x
lp.logLevel = 0
lp.primal(startFinishOptions = 'x')
def tournament(args):
# Configuration
engine = create_engine('sqlite:///' + args.bdd, echo=False)
DBSession.configure(bind=engine)
# On récupere les donnes de la BDD
students = DBSession.query(Etudiant).filter(Etudiant.enseignant.is_(None))
nb_students = DBSession.query(Etudiant).filter(
Etudiant.enseignant.is_(None)).count()
nb_parcours = DBSession.query(Parcours).count()
# Capacité des groupes
capa_min_pec = 14
capa_max_pec = 16
capa_min_pel = 14
capa_max_pel = 16
# On construit le modèle
barre_min = args.b
model = CyClpSimplex()
x = []
s_list = {}
for s in students.all():
v = model.addVariable('etu' + str(s.id), nb_parcours, isInt=True)
x.append(v)
z = 0.0
i = 0
for s in students.all():
reorder = False
s_list[str(i)] = s.id
voeux = DBSession.query(Voeu).filter(Voeu.idEtudiant == s.id).order_by(
Voeu.idParcours).all()
s_voeux = []
rang_q = voeux[nb_parcours - 1].rang
if rang_q != nb_parcours and rang_q != -1:
if s.nom not in special or 'quebec' not in special[s.nom]:
reorder = True
else:
reorder = not special[s.nom]['quebec']
for v in voeux:
if v.rang == -1:
score = 0
else:
if reorder and v.rang > rang_q:
rang = v.rang - 1
elif reorder and v.rang == rang_q:
rang = 9
else:
rang = v.rang
malus = 0.0
if s.malus > 0:
malus += s.malus
if s.absences is not None:
malus += float(s.absences) / 15
score = 2.0**(rang - malus)
s_voeux.append(score)
a = CyLPArray(s_voeux)
b = CyLPArray([1.0 for j in range(nb_parcours)])
z += a * x[i]
model += b * x[i] >= 1
model += b * x[i] <= 1
model += x[i] >= 0
model += x[i] <= 1
i += 1
model.objective = z
# Taille des groupes MpInge (PEL)
for j in [
0, 3, 6
]: # Attention, dans la BDD, le parcours vont de 1 à 8 -> décalage de 1
c = 0
for i in range(nb_students):
c += x[i][j]
model += c <= capa_max_pel
model += c >= capa_min_pel
# Taille des groupes PEC
for j in [1, 2, 4, 5, 7]:
c = 0
for i in range(nb_students):
c += x[i][j]
model += c <= capa_max_pec
model += c >= capa_min_pec
# Etudiants trop bas en maths
i = 0
for s in students.all():
if s.moyenneMaths is not None and s.moyenneMaths < barre_min:
model += x[i][0] + x[i][3] + x[i][6] == 0
i += 1
# Québec
i = 0
for s in students.all():
if s.nom not in special or 'quebec' not in special[s.nom]:
model += x[i][nb_parcours - 1] == 0
i += 1
# Cas particuliers (cf special.py)
i = 0
for s in students.all():
if s.nom in special and 'force' in special[s.nom]:
for cas in special[s.nom]['force']:
if not special[s.nom]['force'][cas]:
model += x[i][int(cas) - 1] == 0
i += 1
cbc_model = model.getCbcModel()
cbc_model.logLevel = 0
cbc_model.branchAndBound()
if cbc_model.isRelaxationOptimal() and args.v:
for s in students.all():
r = cbc_model.primalVariableSolution['etu' + str(s.id)]
print s.nom + '|',
j = 1
reorder = False
for res in r:
if res == 1:
if j == 1 or j == 4 or j == 7:
print "!",
par = DBSession.query(Parcours).filter(
Parcours.id == j).one()
print par.nom,
voeux = DBSession.query(Voeu).filter(
Voeu.idEtudiant == s.id).order_by(
Voeu.idParcours).all()
rang_q = voeux[nb_parcours - 1].rang
if rang_q != nb_parcours and rang_q != -1:
if s.nom not in special or 'quebec' not in special[
s.nom]:
reorder = True
else:
reorder = not special[s.nom]['quebec']
son_voeu = DBSession.query(Voeu).filter(
Voeu.idEtudiant == s.id).filter(
Voeu.idParcours == j).one()
if reorder and son_voeu.rang == rang_q:
rang = 9
elif reorder and son_voeu.rang > rang_q:
rang = son_voeu.rang - 1
else:
rang = son_voeu.rang
print '|' + str(rang) + '',
print '|' + str(s.moyenneMaths),
print '|' + str(s.absences),
if reorder:
print '|*'
else:
print '|'
j += 1
nb_stu_grp = [0 for n in range(nb_parcours)]
rangs_stu = [0 for n in range(nb_parcours)]
indecis = 0
pec2pel = 0
pel2pec = 0
if cbc_model.isRelaxationOptimal() and (args.s or args.csv):
for s in students.all():
r = cbc_model.primalVariableSolution['etu' + str(s.id)]
j = 1
reorder = False
for res in r:
if res == 1:
nb_stu_grp[j - 1] += 1
voeux = DBSession.query(Voeu).filter(
Voeu.idEtudiant == s.id).order_by(
Voeu.idParcours).all()
rang_q = voeux[nb_parcours - 1].rang
if rang_q != nb_parcours and rang_q != -1:
if s.nom not in special or 'quebec' not in special[
s.nom]:
reorder = True
else:
reorder = not special[s.nom]['quebec']
son_voeu = DBSession.query(Voeu).filter(
Voeu.idEtudiant == s.id).filter(
Voeu.idParcours == j).one()
if reorder and son_voeu.rang == rang_q:
rang = 9
elif reorder and son_voeu.rang > rang_q:
rang = son_voeu.rang - 1
else:
rang = son_voeu.rang
if rang != -1:
rangs_stu[rang - 1] += 1
son_voeu_un = DBSession.query(Voeu).filter(
Voeu.idEtudiant == s.id).filter(
Voeu.rang == 1).one()
if son_voeu_un.idParcours in [
1, 4, 7
] and son_voeu.idParcours not in [1, 4, 7]:
pel2pec += 1
elif son_voeu_un.idParcours not in [
1, 4, 7
] and son_voeu.idParcours in [1, 4, 7]:
pec2pel += 1
else:
indecis += 1
j += 1
if args.s:
print "Etudiants par groupe : ", nb_stu_grp
print "Etudiants par rang de voeu : ", rangs_stu
print "Passages PEC->PEL", pec2pel
print "Passage PEL->PEC", pel2pec
print "Indécis : ", indecis
if args.csv:
print barre_min, ",",
for item in nb_stu_grp:
print item, ",",
for item in rangs_stu:
print item, ",",
print pec2pel, ",", pel2pec
if cbc_model.isRelaxationInfeasible():
print "Pas de solution possible"
def solve_ilp(self):
""" Solves problem exactly using MIP/ILP approach
Used solver: CoinOR CBC
Incidence-matrix Q holds complete information needed for opt-process
"""
if self.verbose:
print('Solve: build model')
if self.condorcet_red:
condorcet_red_mat = extended_condorcet_simple(self.votes_arr)
n = self.Q.shape[0]
x_n = n * n
model = CyClpSimplex() # MODEL
x = model.addVariable('x', x_n, isInt=True) # VARS
model.objective = self.Q.ravel() # OBJ
# x_ab = boolean (already int; need to constrain to [0,1])
model += sp.eye(x_n) * x >= np.zeros(x_n)
model += sp.eye(x_n) * x <= np.ones(x_n)
idx = lambda i, j: np.ravel_multi_index((i, j), (n, n))
# constraints for every pair
start_time = time()
n_pairwise_constr = n * (n - 1) // 2
if self.verbose:
print(' # pairwise constr: ', n_pairwise_constr)
# Somewhat bloated just to get some vectorization / speed !
combs_ = combs(range(n), 2)
inds_a = np.ravel_multi_index(combs_.T, (n, n))
inds_b = np.ravel_multi_index(combs_.T[::-1], (n, n))
row_inds = np.tile(np.arange(n_pairwise_constr), 2)
col_inds = np.hstack((inds_a, inds_b))
pairwise_constraints = sp.coo_matrix(
(np.ones(n_pairwise_constr * 2), (row_inds, col_inds)),
shape=(n_pairwise_constr, n * n))
end_time = time()
if self.verbose:
print(" Took {:.{prec}f} secs".format(end_time - start_time,
prec=3))
# and for every cycle of length 3
start_time = time()
n_triangle_constrs = n * (n - 1) * (n - 2)
if self.verbose:
print(' # triangle constr: ', n_triangle_constrs)
# Somewhat bloated just to get some vectorization / speed !
perms_ = perms(range(n), 3)
inds_a = np.ravel_multi_index(perms_.T[(0, 1), :], (n, n))
inds_b = np.ravel_multi_index(perms_.T[(1, 2), :], (n, n))
inds_c = np.ravel_multi_index(perms_.T[(2, 0), :], (n, n))
row_inds = np.tile(np.arange(n_triangle_constrs), 3)
col_inds = np.hstack((inds_a, inds_b, inds_c))
triangle_constraints = sp.coo_matrix(
(np.ones(n_triangle_constrs * 3), (row_inds, col_inds)),
shape=(n_triangle_constrs, n * n))
end_time = time()
if self.verbose:
print(" Took {:.{prec}f} secs".format(end_time - start_time,
prec=3))
model += pairwise_constraints * x == np.ones(n_pairwise_constr)
model += triangle_constraints * x >= np.ones(n_triangle_constrs)
if self.condorcet_red:
I, J, V = sp.find(condorcet_red_mat)
indices_pos = np.ravel_multi_index([J, I], (n, n))
indices_neg = np.ravel_multi_index([I, J], (n, n))
nnz = len(indices_pos)
if self.verbose:
print(
' Extended Condorcet reductions: {} * 2 relations fixed'.
format(nnz))
lhs = sp.coo_matrix(
(np.ones(nnz * 2),
(np.arange(nnz * 2), np.hstack((indices_pos, indices_neg)))),
shape=(nnz * 2, n * n))
rhs = np.hstack(
(np.ones(len(indices_pos)), np.zeros(len(indices_neg))))
model += lhs * x == rhs
cbcModel = model.getCbcModel() # Clp -> Cbc model / LP -> MIP
cbcModel.logLevel = self.verbose
if self.verbose:
print('Solve: run MIP\n')
start_time = time()
status = cbcModel.solve() #-> "Call CbcMain. Solve the problem
# "using the same parameters used
# "by CbcSolver."
# This deviates from cylp's docs which are sparse!
# -> preprocessing will be used and is very important!
end_time = time()
if self.verbose:
print(" CoinOR CBC used {:.{prec}f} secs".format(end_time -
start_time,
prec=3))
x_sol = cbcModel.primalVariableSolution['x']
self.obj_sol = cbcModel.objectiveValue
x = np.array(x_sol).reshape((n, n)).round().astype(int)
self.aggr_rank = np.argsort(x.sum(axis=0))[::-1]
def __init__(self, module_name = None, file_name = None,
A = None, b = None, c = None,
points = None, rays = None,
sense = None, integerIndices = None,
numVars = None):
if file_name is not None:
# We got a file name, so ignore everything else and read in the instance
lp = CyClpSimplex()
lp.extractCyLPModel(file_name)
self.integerIndices = [i for (i, j) in enumerate(lp.integerInformation) if j == True]
infinity = lp.getCoinInfinity()
A = lp.coefMatrix
b = CyLPArray([0 for _ in range(lp.nRows)])
for i in range(lp.nRows):
if lp.constraintsLower[i] > -infinity:
if lp.constraintsUpper[i] < infinity:
raise Exception('Cannot handle ranged constraints')
b[i] = -lp.constraintsLower[i]
for j in range(lp.nCols):
A[i, j] = -A[i, j]
elif lp.constraintsUpper[i] < infinity:
b[i] = lp.constraintsUpper[i]
else:
raise Exception('Constraint with no bounds detected')
x = lp.addVariable('x', lp.nCols)
lp += A * x <= b
lp += x <= lp.variablesUpper
lp += x >= lp.variablesLower
lp.objective = lp.objective
self.sense = '<='
numVars = lp.nCols
else:
min_or_max = None
if module_name is not None:
# We got a module name, read the data from there
mip = ilib.import_module(module_name)
self.A = mip.A if hasattr(mip, 'A') else None
self.b = mip.b if hasattr(mip, 'b') else None
points = mip.points if hasattr(mip, 'points') else None
rays = mip.rays if hasattr(mip, 'rays') else None
self.c = mip.c if hasattr(mip, 'c') else None
self.sense = mip.sense[1] if hasattr(mip, 'sense') else None
min_or_max = mip.sense[0] if hasattr(mip, 'sense') else None
self.integerIndices = mip.integerIndices if hasattr(mip, 'integerIndices') else None
x_u = CyLPArray(mip.x_u) if hasattr(mip, 'x_u') else None
numVars = mip.numVars if hasattr(mip, 'numVars') else None
self.x_sep = mip.x_sep if hasattr(mip, 'x_sep') else None
if numVars is None and mip.A is not None:
numVars = len(mip.A)
if numVars is None:
raise "Must specify number of variables when problem is not"
else:
self.A = A
self.b = b
self.c = c
self.points = points
self.rays = rays
if sense is not None:
self.sense = sense[1]
min_or_max = sense[0]
self.integerIndices = integerIndices
x_u = None
lp = CyClpSimplex()
if self.A is not None:
A = np.matrix(self.A)
b = CyLPArray(self.b)
elif numVars <= 2 and GRUMPY_INSTALLED:
p = Polyhedron2D(points = points, rays = rays)
A = np.matrix(p.hrep.A)
b = np.matrix(p.hrep.b)
else:
raise "Must specify problem in inequality form with more than two variables\n"
#Warning: At the moment, you must put bound constraints in explicitly for split cuts
x_l = CyLPArray([0 for _ in range(numVars)])
x = lp.addVariable('x', numVars)
lp += x >= x_l
if x_u is not None:
lp += x <= x_u
lp += (A * x <= b if self.sense == '<=' else
A * x >= b)
c = CyLPArray(self.c)
if min_or_max == 'Max':
lp.objective = -c * x
else:
lp.objective = c * x
self.lp = lp
self.x = x
