USolver

from returns.result import Success from usolver_mcp.models.cvxpy_models import ( CVXPYConstraint, CVXPYObjective, CVXPYProblem, CVXPYVariable, ObjectiveType, ) from usolver_mcp.solvers.cvxpy_solver import solve_cvxpy_problem pytest_plugins: list[str] = [] def test_simple_linear_regression() -> None: """Test solving a simple linear regression problem using CVXPY. Problem: minimize ||Ax - b||² where A = [[1, 1], [2, 1], [3, 1]] and b = [3, 5, 7] Expected solution: x ≈ [2, 1] (slope=2, intercept=1) """ problem = CVXPYProblem( variables=[CVXPYVariable(name="x", shape=2)], objective=CVXPYObjective( type=ObjectiveType.MINIMIZE, expression="cp.sum_squares(np.array(A) @ x - np.array(b))", ), constraints=[], parameters={"A": [[1, 1], [2, 1], [3, 1]], "b": [3, 5, 7]}, description="Simple linear regression problem", ) result = solve_cvxpy_problem(problem) assert isinstance(result, Success) solution = result.unwrap() # Check that the problem was solved successfully assert solution.status == "optimal" assert solution.objective_value is not None assert solution.objective_value < 1e-10 # Should be very close to 0 # Check solution values (should be approximately [2, 1]) x_values = solution.values["x"] assert abs(x_values[0] - 2.0) < 1e-6 # slope assert abs(x_values[1] - 1.0) < 1e-6 # intercept def test_constrained_optimization() -> None: """Test CVXPY with constraints. Problem: minimize x² + y² subject to: x + y >= 2, x >= 0, y >= 0 Expected solution: x = 1, y = 1, objective = 2 """ problem = CVXPYProblem( variables=[CVXPYVariable(name="x", shape=1), CVXPYVariable(name="y", shape=1)], objective=CVXPYObjective( type=ObjectiveType.MINIMIZE, expression="cp.sum_squares(x) + cp.sum_squares(y)", ), constraints=[ CVXPYConstraint(expression="x + y >= 2"), CVXPYConstraint(expression="x >= 0"), CVXPYConstraint(expression="y >= 0"), ], description="Constrained quadratic optimization", ) result = solve_cvxpy_problem(problem) assert isinstance(result, Success) solution = result.unwrap() assert solution.status == "optimal" assert solution.objective_value is not None # Check solution values (should be approximately x=1, y=1) x_val = ( solution.values["x"][0] if hasattr(solution.values["x"], "__len__") else solution.values["x"] ) y_val = ( solution.values["y"][0] if hasattr(solution.values["y"], "__len__") else solution.values["y"] ) assert abs(x_val - 1.0) < 1e-4 assert abs(y_val - 1.0) < 1e-4 assert abs(solution.objective_value - 2.0) < 1e-4 def test_portfolio_optimization() -> None: """Test a simple portfolio optimization problem. Problem: minimize risk while achieving target return Two assets with different risk/return profiles """ problem = CVXPYProblem( variables=[CVXPYVariable(name="weights", shape=2)], objective=CVXPYObjective( type=ObjectiveType.MINIMIZE, expression="cp.quad_form(weights, cov_matrix)" ), constraints=[ CVXPYConstraint(expression="cp.sum(weights) == 1"), # weights sum to 1 CVXPYConstraint(expression="weights >= 0"), # long-only CVXPYConstraint( expression="np.array(returns).T @ weights >= target_return" ), # target return ], parameters={ "cov_matrix": [[0.04, 0.01], [0.01, 0.09]], # covariance matrix "returns": [0.08, 0.12], # expected returns "target_return": 0.10, # target 10% return }, description="Simple portfolio optimization", ) result = solve_cvxpy_problem(problem) assert isinstance(result, Success) solution = result.unwrap() assert solution.status == "optimal" assert solution.objective_value is not None # Check that weights sum to 1 weights = solution.values["weights"] assert abs(sum(weights) - 1.0) < 1e-6 # Check that all weights are non-negative assert all(w >= -1e-6 for w in weights) def test_infeasible_problem() -> None: """Test CVXPY with an infeasible problem. Problem: minimize x subject to: x >= 2, x <= 1 This should be infeasible. """ problem = CVXPYProblem( variables=[CVXPYVariable(name="x", shape=1)], objective=CVXPYObjective(type=ObjectiveType.MINIMIZE, expression="x"), constraints=[ CVXPYConstraint(expression="x >= 2"), CVXPYConstraint(expression="x <= 1"), ], description="Infeasible optimization problem", ) result = solve_cvxpy_problem(problem) assert isinstance(result, Success) solution = result.unwrap() # Should be infeasible assert solution.status == "infeasible" assert solution.objective_value is None or solution.objective_value == float("inf")