def create_lp(polyt, obj_inds):
""" Creates core LP problem with the Stoichiometric Matrix and list of constraints"""
# create problem
p = qsoptex.ExactProblem()
[Aeq, beq, rids,
domain] = [polyt["Aeq"], polyt["beq"], polyt["rids"], polyt["domain"]]
[lbs, ubs] = domain
# add variables to lp
for i in range(len(rids)):
p.add_variable(name=rids[i],
objective=Fraction(str(obj_inds[i])),
lower=lbs[i],
upper=ubs[i])
# constraints
# for each row in S (metabolite) = for each constraint
for i in range(Aeq.shape[0]):
constr = {}
# for each column in S = for each reaction
for j in range(Aeq.shape[1]):
if Aeq[i, j] != 0:
constr[rids[j]] = int(Aeq[i, j])
p.add_linear_constraint(qsoptex.ConstraintSense.EQUAL,
constr,
rhs=int(beq[i]))
return p
def l1_equiv_lp(N, constraint_rhs, objective_weight_vector=None, variable_lower_bound=None, variable_upper_bound=None):
m, n = getSize(N)
# following part set a default weight vector
if objective_weight_vector is None:
objective_weight_vector = np.ones(n)
if variable_lower_bound is None:
variable_lower_bound = [None]*n
if variable_upper_bound is None:
variable_upper_bound = [None]*n
p = qsoptex.ExactProblem()
# In the following part we define the objective of the linear programming
for i in range(n):
p.add_variable(name='z' + str(i), objective=objective_weight_vector[i], lower=None, upper=None)
p.add_variable(name='x' + str(i), objective=0, lower=variable_lower_bound[i], upper=variable_upper_bound[i])
# In the following part we define constrains of the linear Programming
for j in range(m):
p.add_linear_constraint(qsoptex.ConstraintSense.GREATER,
{'x' + str(i): N[j][i] for i in range(n)}, rhs=constraint_rhs[j])
for i in range(n):
p.add_linear_constraint(qsoptex.ConstraintSense.GREATER,
{'z' + str(i): 1, 'x' + str(i): 1}, rhs=0)
p.add_linear_constraint(qsoptex.ConstraintSense.GREATER,
{'z' + str(i): 1, 'x' + str(i): -1}, rhs=0)
# Following part would solve the LP with qsoptex
p.set_objective_sense(qsoptex.ObjectiveSense.MINIMIZE)
p.set_param(qsoptex.Parameter.SIMPLEX_DISPLAY, 1)
status = p.solve()
return p, status, n
def minimum_free_lunches(N, constraint_rhs, objective_weight_vector=None, variable_lower_bound=None, variable_upper_bound=None):
m, n = getSize(N)
# alpha = Fraction(1, 10)
# Set default weight vector
if objective_weight_vector is None:
objective_weight_vector = np.ones(n)
if variable_lower_bound is None:
variable_lower_bound = [None]*n
if variable_upper_bound is None:
variable_upper_bound = [None]*n
p = qsoptex.ExactProblem()
for i in range(n):
p.add_variable(name='z' + str(i), objective=objective_weight_vector[i], lower=None, upper=None)
p.add_variable(name='x' + str(i), objective=0, lower=variable_lower_bound[i], upper=variable_upper_bound[i])
# The following part will add non-negative slack variable y
for i in range(m):
p.add_variable(name='y' + str(i), objective=0, lower=0, upper=None)
for j in range(m):
constraints_dict = {'x' + str(i): N[j][i] for i in range(n)}
constraints_dict.update({'y' + str(j): -1})
p.add_linear_constraint(qsoptex.ConstraintSense.EQUAL, constraints_dict, rhs=constraint_rhs[j])
constraints_dict.clear()
for i in range(n):
p.add_linear_constraint(qsoptex.ConstraintSense.GREATER,
{'z' + str(i): 1, 'x' + str(i): 1}, rhs=0)
p.add_linear_constraint(qsoptex.ConstraintSense.GREATER,
{'z' + str(i): 1, 'x' + str(i): -1}, rhs=0)
# Adding tolerance to whole system
p.add_linear_constraint(qsoptex.ConstraintSense.GREATER, {'y' + str(i): 1 for i in range(m)},
rhs=1)
# ------------
p.set_objective_sense(qsoptex.ObjectiveSense.MINIMIZE)
p.set_param(qsoptex.Parameter.SIMPLEX_DISPLAY, 1)
status = p.solve()
return p, status, n
def __init__(self, **kwargs):
self._p = qsoptex.ExactProblem()
self._p.set_param(qsoptex.Parameter.SIMPLEX_DISPLAY, 1)
self._variables = {}
self._var_names = ('x' + str(i) for i in count(1))
self._constr_names = ('c' + str(i) for i in count(1))
self._result = None
def setUp(self):
self._problem = qsoptex.ExactProblem()
self._problem.add_variable(name='x',
objective=2,
lower=3.5,
upper=17.5)
self._problem.add_variable(name='y', objective=-1, lower=None, upper=2)
self._problem.add_linear_constraint(qsoptex.ConstraintSense.EQUAL, {
'x': 1,
'y': 1
},
rhs=0,
name='c1')
self._problem.set_objective_sense(qsoptex.ObjectiveSense.MAXIMIZE)
def test_variable_unicode(self):
"""Test that a variable of unicode type works"""
self._problem = qsoptex.ExactProblem()
self._problem.add_variable(name=u('x'))
def test_variable_str(self):
"""Test that a variable of bytes type works"""
self._problem = qsoptex.ExactProblem()
self._problem.add_variable(name=b('x'))