def test_dual_integer_vars_as_lp(self, model):
var = _add_decision_variable(model, "AKGDH")
assert abs(model.optimize("maximize").f - 0.874) < 0.001
# as lp: make integer continuous, set to 1
dual = dual_problem(model, "maximize", [var.id], copy=True)
r = dual.reactions.get_by_id(var.id)
r.variable_kind = "continuous"
r.lower_bound = r.upper_bound = 1
assert abs(dual.optimize("minimize").f - 0.874) < 0.001
r.lower_bound = r.upper_bound = 0
assert abs(dual.optimize("minimize").f - 0.858) < 0.001
def test_dual_integer_vars_as_lp(self, model):
var = _add_decision_variable(model, "AKGDH")
assert abs(model.optimize("maximize").f - 0.874) < 0.001
# as lp: make integer continuous, set to 1
dual = dual_problem(model, "maximize", [var.id], copy=True)
r = dual.reactions.get_by_id(var.id)
r.variable_kind = "continuous"
r.lower_bound = r.upper_bound = 1
assert abs(dual.optimize("minimize").f - 0.874) < 0.001
r.lower_bound = r.upper_bound = 0
assert abs(dual.optimize("minimize").f - 0.858) < 0.001
def test_dual_integer_vars_as_lp(self):
model = create_test_model("textbook")
var = _add_decision_variable(model, "AKGDH")
self.assertAlmostEqual(model.optimize("maximize").f, 0.874, places=3)
# as lp: make integer continuous, set to 1
dual = dual_problem(model, "maximize", [var.id], copy=True)
r = dual.reactions.get_by_id(var.id)
r.variable_kind = "continuous"
r.lower_bound = r.upper_bound = 1
self.assertAlmostEqual(dual.optimize("minimize").f, 0.874, places=3)
r.lower_bound = r.upper_bound = 0
self.assertAlmostEqual(dual.optimize("minimize").f, 0.858, places=3)
def test_dual_integer_vars_as_mip(self, model):
# mip
var = _add_decision_variable(model, "AKGDH")
dual = dual_problem(model, "maximize", [var.id], copy=True)
var_in_dual = dual.reactions.get_by_id(var.id)
# minimization, so the optimal value state is to turn off AKGDH
assert abs(dual.optimize("minimize").f - 0.858) < 0.001
# turn off AKGDH in dual
var_in_dual.lower_bound = var_in_dual.upper_bound = 1
assert abs(dual.optimize("minimize").f - 0.874) < 0.001
# turn on AKGDH in dual
var_in_dual.lower_bound = var_in_dual.upper_bound = 0
assert abs(dual.optimize("minimize").f - 0.858) < 0.001
def test_dual_integer_vars_as_mip(self, model):
# mip
var = _add_decision_variable(model, "AKGDH")
dual = dual_problem(model, "maximize", [var.id], copy=True)
var_in_dual = dual.reactions.get_by_id(var.id)
# minimization, so the optimal value state is to turn off AKGDH
assert abs(dual.optimize("minimize").f - 0.858) < 0.001
# turn off AKGDH in dual
var_in_dual.lower_bound = var_in_dual.upper_bound = 1
assert abs(dual.optimize("minimize").f - 0.874) < 0.001
# turn on AKGDH in dual
var_in_dual.lower_bound = var_in_dual.upper_bound = 0
assert abs(dual.optimize("minimize").f - 0.858) < 0.001
def test_dual_integer_vars_as_mip(self):
# mip
model = create_test_model("textbook")
var = _add_decision_variable(model, "AKGDH")
dual = dual_problem(model, "maximize", [var.id], copy=True)
var_in_dual = dual.reactions.get_by_id(var.id)
# minimization, so the optimal value state is to turn off AKGDH
self.assertAlmostEqual(dual.optimize("minimize").f, 0.858, places=3)
# turn off AKGDH in dual
var_in_dual.lower_bound = var_in_dual.upper_bound = 1
self.assertAlmostEqual(dual.optimize("minimize").f, 0.874, places=3)
# turn on AKGDH in dual
var_in_dual.lower_bound = var_in_dual.upper_bound = 0
self.assertAlmostEqual(dual.optimize("minimize").f, 0.858, places=3)
