def run():
primal = Model("example_06_dual")
x0 = primal.create_variable("x0")
x1 = primal.create_variable("x1")
x2 = primal.create_variable("x2")
primal.add_constraint(4*x0 + 8*x1 - x2 <= 5)
primal.add_constraint(7*x0 - 2*x1 + 2*x2 >= 4)
primal.maximize(3*x0 + 2*x1 - 6*x2)
expected_dual = Model("example_06_dual (expected dual)")
y0 = expected_dual.create_variable("y0")
y1 = expected_dual.create_variable("y1")
expected_dual.add_constraint(4*y0 - 7*y1 >= 3)
expected_dual.add_constraint(8*y0 + 2*y1 >= 2)
expected_dual.add_constraint(-1*y0 - 2*y1 >= -6)
expected_dual.minimize(5 * y0 - 4 * y1)
dual = primal.dual()
assert dual.is_equivalent(expected_dual), "dual wasn't calculated as expected"
assert primal.is_equivalent(dual.dual()), "double dual should equal the initial model"
primal_solution = primal.solve()
dual_solution = dual.solve()
assert primal_solution.objective_value() == dual_solution.objective_value(), "dual and primal should have the same value at optimum"
logging.info("Congratulations! The dual creation seems to be implemented correctly :)")
def run():
primal = Model("Zad2")
# x1 -> ławki
# x2 -> stoły
# x3 -> krzesła
x1 = primal.create_variable("x1")
x2 = primal.create_variable("x2")
x3 = primal.create_variable("x3")
primal.add_constraint(8 * x1 + 6 * x2 + x3 <= 960)
primal.add_constraint(8 * x1 + 4 * x2 + 3 * x3 <= 800)
primal.add_constraint(4 * x1 + 3 * x2 + x3 <= 320)
primal.maximize(60 * x1 + 30 * x2 + 20 * x3)
expected_dual = Model("Zad2")
y1 = expected_dual.create_variable("y1")
y2 = expected_dual.create_variable("y2")
y3 = expected_dual.create_variable("y3")
expected_dual.add_constraint(8 * y1 + 8 * y2 + 4 * y3 >= 60)
expected_dual.add_constraint(6 * y1 + 4 * y2 + 3 * y3 >= 30)
expected_dual.add_constraint(y1 + 3 * y2 + y3 >= 20)
expected_dual.minimize(960 * y1 + 800 * y2 + 320 * y3)
expected_bounds = [(56.0, 80.0), (float("-inf"), 35.0), (15.0, 22.5)]
dual = primal.dual()
assert dual.is_equivalent(
expected_dual), "dual wasn't calculated as expected"
assert primal.is_equivalent(
dual.dual()), "double dual should equal the initial model"
primal_solution = primal.solve()
dual_solution = dual.solve()
assert math.isclose(
primal_solution.objective_value(),
dual_solution.objective_value(),
abs_tol=0.000001
), "dual and primal should have the same value at optimum"
logging.info(
"Congratulations! The dual creation seems to be implemented correctly :)\n"
)
analyser = Analyser()
analysis_results = analyser.analyse(primal_solution)
analyser.interpret_results(primal_solution, analysis_results, logging.info)
objective_analysis_results = analysis_results[
ObjectiveSensitivityAnalyser.name()]
tolerance = 0.001
for (i, bounds_pair) in enumerate(objective_analysis_results):
assert math.isclose(
bounds_pair[0], expected_bounds[i][0], abs_tol=tolerance
), f"left bound of the coefficient range seems to be incorrect, expected {expected_bounds[i][0]}, got {bounds_pair[0]}"
assert math.isclose(
bounds_pair[1], expected_bounds[i][1], abs_tol=tolerance
), f"right bound of the coefficient range seems to be incorrect, expected {expected_bounds[i][1]}, got {bounds_pair[1]}"
logging.info(
"Congratulations! This cost coefficients analysis look alright :)")
print(dual)
print(analyser.interpret_results(primal_solution, analysis_results))
def run():
primal = Model("Zad1")
# x1 -> SS - super
# x2 -> S - standardowy
# x3 -> O - oszczędny
x1 = primal.create_variable("x1")
x2 = primal.create_variable("x2")
x3 = primal.create_variable("x3")
primal.add_constraint(2 * x1 + 2 * x2 + 5 * x3 <= 40)
primal.add_constraint(x1 + 3 * x2 + 2 * x3 <= 30)
primal.add_constraint(3 * x1 + x2 + 3 * x3 <= 30)
primal.maximize(32 * x1 + 24 * x2 + 48 * x3)
expected_dual = Model("Zad1")
y1 = expected_dual.create_variable("y1")
y2 = expected_dual.create_variable("y2")
y3 = expected_dual.create_variable("y3")
expected_dual.add_constraint(2 * y1 + y2 + 3 * y3 >= 32)
expected_dual.add_constraint(2 * y1 + 3 * y2 + y3 >= 24)
expected_dual.add_constraint(5 * y1 + 2 * y2 + 3 * y3 >= 48)
expected_dual.minimize(40 * y1 + 30 * y2 + 30 * y3)
expected_bounds = [(19.6363636363, 44.57142857),
(17.77777777, 53.333333333), (37.0, 61.99999999999)]
dual = primal.dual()
assert dual.is_equivalent(
expected_dual), "dual wasn't calculated as expected"
assert primal.is_equivalent(
dual.dual()), "double dual should equal the initial model"
primal_solution = primal.solve()
dual_solution = dual.solve()
assert math.isclose(
primal_solution.objective_value(),
dual_solution.objective_value(),
abs_tol=0.000001
), "dual and primal should have the same value at optimum"
logging.info(
"Congratulations! The dual creation seems to be implemented correctly :)\n"
)
analyser = Analyser()
analysis_results = analyser.analyse(primal_solution)
analyser.interpret_results(primal_solution, analysis_results, logging.info)
objective_analysis_results = analysis_results[
ObjectiveSensitivityAnalyser.name()]
tolerance = 0.001
for (i, bounds_pair) in enumerate(objective_analysis_results):
assert math.isclose(
bounds_pair[0], expected_bounds[i][0], abs_tol=tolerance
), f"left bound of the coefficient range seems to be incorrect, expected {expected_bounds[i][0]}, got {bounds_pair[0]}"
assert math.isclose(
bounds_pair[1], expected_bounds[i][1], abs_tol=tolerance
), f"right bound of the coefficient range seems to be incorrect, expected {expected_bounds[i][1]}, got {bounds_pair[1]}"
logging.info(
"Congratulations! This cost coefficients analysis look alright :)")
print(dual)
print(analyser.interpret_results(primal_solution, analysis_results))