aefba06234c63a950bac21c9d4f07e97ebed7d5f,qiskit/optimization/algorithms/minimum_eigen_optimizer.py,MinimumEigenOptimizer,solve,#MinimumEigenOptimizer#Any#,128

Before Change

        samples = eigenvector_to_solutions(eigen_results.eigenstate, operator)
        samples = [(res[0], problem_.objective.sense.value * (res[1] + offset), res[2])
                   for res in samples]
        samples.sort(key=lambda x: problem_.objective.sense.value * x[1])

        // translate result back to integers
        opt_res = MinimumEigenOptimizerResult(samples[0][0], samples[0][1], samples, qubo_converter)
        opt_res = qubo_converter.decode(opt_res)

After Change

        operator, offset = operator_converter.encode(problem_)

        // only try to solve non-empty Ising Hamiltonians
        if operator.num_qubits > 0:

            // approximate ground state of operator using min eigen solver
            eigen_results = self._min_eigen_solver.compute_minimum_eigenvalue(operator)

            // analyze results
            samples = eigenvector_to_solutions(eigen_results.eigenstate, operator)
            samples = [(res[0], problem_.objective.sense.value * (res[1] + offset), res[2])
                       for res in samples]
            samples.sort(key=lambda x: problem_.objective.sense.value * x[1])
            x = samples[0][0]
            fval = samples[0][1]

        // if Hamiltonian is empty, then the objective function is constant to the offset
        else:
            x = [0]*problem_.get_num_binary_vars()
            fval = offset
            x_str = "0"*problem_.get_num_binary_vars()
            samples = [(x_str, offset, 1.0)]

        // translate result back to integers
        opt_res = MinimumEigenOptimizerResult(x, fval, samples, qubo_converter)
        opt_res = qubo_converter.decode(opt_res)

        // translate results back to original problem

Instances