Python cvxpy.SolverError() Examples

The following are 6 code examples of cvxpy.SolverError(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module cvxpy , or try the search function .
Example #1
Source File: scproblem.py    From SuccessiveConvexificationFreeFinalTime with MIT License 6 votes vote down vote up 
def solve(self, **kwargs):
        error = False
        try:
            self.prob.solve(**kwargs)
        except cvx.SolverError:
            error = True

        stats = self.prob.solver_stats
        print()

        info = {
            'setup_time': stats.setup_time,
            'solver_time': stats.solve_time,
            'iterations': stats.num_iters,
            'solver_error': error
        }

        return info
Example #2
Source File: test_measures.py    From qiskit-terra with Apache License 2.0 6 votes vote down vote up 
def test_diamond_norm(self, num_qubits):
        """Test the diamond_norm for {num_qubits}-qubit pauli channel."""
        # pylint: disable=import-outside-toplevel
        try:
            import cvxpy
        except ImportError:
            # Skip test if CVXPY not installed
            self.skipTest("CVXPY not installed.")

        # Pauli channels have an analytic expression for the
        # diamond norm so we can easily test it
        op = Choi(np.zeros((4 ** num_qubits, 4 ** num_qubits)))
        labels = [num_qubits * i for i in ['I', 'X', 'Y', 'Z']]
        coeffs = [-1.0, 0.5, 2.5, -0.1]
        for coeff, label in zip(coeffs, labels):
            op = op + coeff * Choi(Operator.from_label(label))
        target = np.sum(np.abs(coeffs))

        try:
            value = diamond_norm(op)
            self.assertAlmostEqual(value, target, places=4)
        except cvxpy.SolverError:
            self.skipTest("CVXPY solver failed.")
Example #3
Source File: scproblem.py    From SCvx with MIT License 5 votes vote down vote up 
def solve(self, **kwargs):
        error = False
        try:
            self.prob.solve(**kwargs)
        except cvx.SolverError:
            error = True

        return error
Example #4
Source File: scproblem.py    From SCvx with MIT License 5 votes vote down vote up 
def solve(self, **kwargs):
        error = False
        try:
            self.prob.solve(**kwargs)
        except cvx.SolverError:
            error = True

        return error
Example #5
Source File: test_demographic_parity.py    From scikit-lego with MIT License 5 votes vote down vote up 
def _test_same(dataset):
    X, y = dataset
    if X.shape[1] == 1:
        # If we only have one column (which is also the sensitive one) we can't fit
        return True

    sensitive_cols = [0]
    X_without_sens = np.delete(X, sensitive_cols, axis=1)
    lr = LogisticRegression(
        penalty="none",
        solver="lbfgs",
        multi_class="ovr",
        dual=False,
        tol=1e-4,
        C=1.0,
        fit_intercept=True,
        intercept_scaling=1,
        class_weight=None,
        random_state=None,
        max_iter=100,
        verbose=0,
        warm_start=False,
        n_jobs=None,
        l1_ratio=None,
    )
    fair = DemographicParityClassifier(
        covariance_threshold=None, sensitive_cols=sensitive_cols, penalty="none"
    )
    try:
        fair.fit(X, y)
    except SolverError:
        pass
    else:
        lr.fit(X_without_sens, y)
        normal_pred = lr.predict_proba(X_without_sens)
        fair_pred = fair.predict_proba(X)
        np.testing.assert_almost_equal(normal_pred, fair_pred, decimal=3)
        assert np.sum(lr.predict(X_without_sens) != fair.predict(X)) / len(X) < 0.01
Example #6
Source File: ecos.py    From osqp_benchmarks with Apache License 2.0 4 votes vote down vote up 
def solve(self, example):
        '''
        Solve problem

        Args:
            example: example object

        Returns:
            Results structure
        '''
        problem = example.cvxpy_problem

        if 'verbose' in self._settings:
            verbose = self._settings["verbose"]
        else:
            verbose = False

        try:
            obj_val = problem.solve(solver=cvxpy.ECOS, verbose=verbose)
        except cvxpy.SolverError:
            if 'verbose' in self._settings:  # if verbose is null, suppress it
                if self._settings['verbose']:
                    print("Error in ECOS solution\n")
            return Results(s.SOLVER_ERROR, None, None, None,
                           None, None)

        status = self.STATUS_MAP.get(problem.status, s.SOLVER_ERROR)

        # Obtain time and number of iterations
        run_time = problem.solver_stats.solve_time \
            + problem.solver_stats.setup_time

        niter = problem.solver_stats.num_iters

        # Get primal, dual solution
        x, y = example.revert_cvxpy_solution()

        # Validate status
        if not is_qp_solution_optimal(example.qp_problem, x, y,
                                      high_accuracy=self._settings.get('high_accuracy')):
            status = s.SOLVER_ERROR

        # Validate execution time
        if 'time_limit' in self._settings:
            if run_time > self._settings['time_limit']:
                status = s.TIME_LIMIT

        return Results(status,
                       obj_val,
                       x,
                       y,
                       run_time,
                       niter)