def test_change_of_objective_is_reflected_in_low_level_solver(self):
x = Variable('x', lb=-83.3, ub=1324422.)
y = Variable('y', lb=-181133.3, ub=12000.)
objective = Objective(0.3 * x + 0.4 * y, name='test', direction='max')
self.model.objective = objective
self.assertEqual((self.model.objective.expression -
(0.4 * y + 0.3 * x)).expand() - 0, 0)
self.assertEqual(self.model.objective.direction, "max")
self.assertEqual(glp_get_obj_coef(self.model.problem, x._index), 0.3)
self.assertEqual(glp_get_obj_coef(self.model.problem, y._index), 0.4)
for i in range(1, glp_get_num_cols(self.model.problem) + 1):
if i != x._index and i != y._index:
self.assertEqual(glp_get_obj_coef(self.model.problem, i), 0)
z = Variable('z', lb=4, ub=4, type='integer')
self.model.objective += 77. * z
self.assertEqual((self.model.objective.expression -
(0.4 * y + 0.3 * x + 77.0 * z)).expand() - 0, 0)
self.assertEqual(self.model.objective.direction, "max")
self.assertEqual(glp_get_obj_coef(self.model.problem, x._index), 0.3)
self.assertEqual(glp_get_obj_coef(self.model.problem, y._index), 0.4)
self.assertEqual(glp_get_obj_coef(self.model.problem, z._index), 77.)
for i in range(1, glp_get_num_cols(self.model.problem) + 1):
if i != x._index and i != y._index and i != z._index:
self.assertEqual(glp_get_obj_coef(self.model.problem, i), 0)
def _MakeMDFProblem(self):
"""Create a CVXOPT problem for finding the Maximal Thermodynamic
Driving Force (MDF).
Does not set the objective function... leaves that to the caller.
Returns:
the linear problem object, and the three types of variables as arrays
"""
A, b, c, y, l = self._GetPrimalVariablesAndConstants()
B = Variable('mdf')
x = y + l + [B]
lp = Model(name="MDF_PRIMAL")
cnstr_names = ["driving_force_%02d" % j for j in range(self.Nr_active)] + \
["covariance_var_ub_%02d" % j for j in range(self.Nr)] + \
["covariance_var_lb_%02d" % j for j in range(self.Nr)] + \
["log_conc_ub_%02d" % j for j in range(self.Nc)] + \
["log_conc_lb_%02d" % j for j in range(self.Nc)]
constraints = []
for j in range(A.shape[0]):
row = [A[j, i] * x[i] for i in range(A.shape[1])]
constraints.append(
Constraint(sum(row), ub=b[j, 0], name=cnstr_names[j]))
lp.add(constraints)
row = [c[i, 0] * x[i] for i in range(c.shape[0])]
lp.objective = Objective(sum(row), direction='max')
return lp, y, l, B
def _MakeMDFProblemDual(self):
"""Create a CVXOPT problem for finding the Maximal Thermodynamic
Driving Force (MDF).
Does not set the objective function... leaves that to the caller.
Returns:
the linear problem object, and the four types of variables as arrays
"""
A, b, c, w, g, z, u = self._GetDualVariablesAndConstants()
x = w + g + z + u
lp = Model(name="MDF_DUAL")
cnstr_names = ["y_%02d" % j for j in range(self.Nr)] + \
["l_%02d" % j for j in range(self.Nc)] + \
["MDF"]
constraints = []
for i in range(A.shape[1]):
row = [A[j, i] * x[j] for j in range(A.shape[0])]
constraints.append(
Constraint(sum(row),
lb=c[i, 0],
ub=c[i, 0],
name=cnstr_names[i]))
lp.add(constraints)
row = [b[i, 0] * x[i] for i in range(A.shape[0])]
lp.objective = Objective(sum(row), direction='min')
return lp, w, g, z, u
def setUp(self):
self.var1 = var1 = Variable("var1", lb=0, ub=1, type="continuous")
self.var2 = var2 = Variable("var2", lb=0, ub=1, type="continuous")
self.const1 = const1 = Constraint(0.5 * var1, lb=0, ub=1, name="c1")
self.const2 = const2 = Constraint(0.1 * var2 + 0.4 * var1, name="c2")
self.model = model = Model()
model.add([var1, var2])
model.add([const1, const2])
model.objective = Objective(var1 + var2)
model.update()
self.json_string = json.dumps(model.to_json())
def _GetTotalEnergyProblem(self, min_driving_force=0.0, direction='min'):
A, b, _c, y, l = self._GetPrimalVariablesAndConstants()
x = y + l + [min_driving_force]
lp = Model(name='MDF')
constraints = []
for j in range(A.shape[0]):
row = [A[j, i] * x[i] for i in range(A.shape[1])]
constraints.append(
Constraint(sum(row), ub=b[j, 0], name='row_%d' % j))
total_g0 = float(self.fluxes @ self.dG0_r_prime)
total_reaction = self.S @ self.fluxes.T
row = [total_reaction[i, 0] * x[i] for i in range(self.Nc)]
total_g = total_g0 + sum(row)
lp.add(constraints)
lp.objective = Objective(total_g, direction=direction)
return lp