# Buggy code
def solve(self, problem: QuadraticProgram) -> ADMMOptimizationResult:
    """Tries to solves the given problem using ADMM algorithm.

    Args:
        problem: The problem to be solved.

    Returns:
        The result of the optimizer applied to the problem.

    Raises:
        QiskitOptimizationError: If the problem is not compatible with the ADMM optimizer.
    """
    self._verify_compatibility(problem)

    # debug
    self._log.debug("Initial problem: %s", problem.export_as_lp_string())

    # map integer variables to binary variables
    from ..converters.integer_to_binary import IntegerToBinary

    int2bin = IntegerToBinary()
    original_variables = problem.variables
    problem = int2bin.convert(problem)

    # we deal with minimization in the optimizer, so turn the problem to minimization
    problem, sense = self._turn_to_minimization(problem)

    # create our computation state.
    self._state = ADMMState(problem, self._params.rho_initial)

    # parse problem and convert to an ADMM specific representation.
    self._state.binary_indices = self._get_variable_indices(
        problem, Variable.Type.BINARY
    )
    self._state.continuous_indices = self._get_variable_indices(
        problem, Variable.Type.CONTINUOUS
    )
    if self._params.warm_start:
        # warm start injection for the initial values of the variables
        self._warm_start(problem)

    # convert optimization problem to a set of matrices and vector that are used
    # at each iteration.
    self._convert_problem_representation()

    start_time = time.time()
    # we have not stated our computations yet, so elapsed time initialized as zero.
    elapsed_time = 0.0
    iteration = 0
    residual = 1.0e2

    while (iteration < self._params.maxiter and residual > self._params.tol) and (
        elapsed_time < self._params.max_time
    ):
        if self._state.step1_absolute_indices:
            op1 = self._create_step1_problem()
            self._state.x0 = self._update_x0(op1)
            # debug
            self._log.debug("Step 1 sub-problem: %s", op1.export_as_lp_string())
        # else, no binary variables exist, and no update to be done in this case.
        # debug
        self._log.debug("x0=%s", self._state.x0)

        op2 = self._create_step2_problem()
        self._state.u, self._state.z = self._update_x1(op2)
        # debug
        self._log.debug("Step 2 sub-problem: %s", op2.export_as_lp_string())
        self._log.debug("u=%s", self._state.u)
        self._log.debug("z=%s", self._state.z)

        if self._params.three_block:
            if self._state.binary_indices:
                op3 = self._create_step3_problem()
                self._state.y = self._update_y(op3)
                # debug
                self._log.debug("Step 3 sub-problem: %s", op3.export_as_lp_string())
            # debug
            self._log.debug("y=%s", self._state.y)

        self._state.lambda_mult = self._update_lambda_mult()
        # debug
        self._log.debug("lambda: %s", self._state.lambda_mult)

        cost_iterate = self._get_objective_value()
        constraint_residual = self._get_constraint_residual()
        residual, dual_residual = self._get_solution_residuals(iteration)
        merit = self._get_merit(cost_iterate, constraint_residual)
        # debug
        self._log.debug(
            "cost_iterate=%s, cr=%s, merit=%s", cost_iterate, constraint_residual, merit
        )

        # costs are saved with their original sign.
        self._state.cost_iterates.append(cost_iterate)
        self._state.residuals.append(residual)
        self._state.dual_residuals.append(dual_residual)
        self._state.cons_r.append(constraint_residual)
        self._state.merits.append(merit)
        self._state.lambdas.append(np.linalg.norm(self._state.lambda_mult))

        self._state.x0_saved.append(self._state.x0)
        self._state.u_saved.append(self._state.u)
        self._state.z_saved.append(self._state.z)
        self._state.z_saved.append(self._state.y)

        self._update_rho(residual, dual_residual)

        iteration += 1
        elapsed_time = time.time() - start_time

    binary_vars, continuous_vars, objective_value = self._get_best_merit_solution()
    solution = self._revert_solution_indexes(binary_vars, continuous_vars)

    # flip the objective sign again if required
    objective_value = objective_value * sense

    # convert back integer to binary
    base_result = OptimizationResult(
        solution, objective_value, original_variables, OptimizationResultStatus.SUCCESS
    )
    base_result = int2bin.interpret(base_result)

    # third parameter is our internal state of computations.
    result = ADMMOptimizationResult(
        x=base_result.x,
        fval=base_result.fval,
        variables=base_result.variables,
        state=self._state,
        status=self._get_feasibility_status(problem, base_result.x),
    )

    # debug
    self._log.debug(
        "solution=%s, objective=%s at iteration=%s",
        solution,
        objective_value,
        iteration,
    )
    return result


# Traceback (qiskit.optimization.exceptions.QiskitOptimizationError)
Traceback (most recent call last):
File ".../admmprove.py", line 53, in <module>
result_q = admm_q.solve(qp)
File ".../qiskit-aqua/qiskit/optimization/algorithms/admm_optimizer.py", line 381, in solve
OptimizationResultStatus.SUCCESS)
File ".../qiskit-aqua/qiskit/optimization/algorithms/optimization_algorithm.py", line 106, in __init__
[v.name for v in variables]))
qiskit.optimization.exceptions.QiskitOptimizationError: "Inconsistent size of optimal value and variables. x: size 6 [0. 0. 0. 0. 0. 0.], variables: size 4 ['v', 'w', 't', 'u']"

# Fixed code
def solve(self, problem: QuadraticProgram) -> ADMMOptimizationResult:
    """Tries to solves the given problem using ADMM algorithm.

    Args:
        problem: The problem to be solved.

    Returns:
        The result of the optimizer applied to the problem.

    Raises:
        QiskitOptimizationError: If the problem is not compatible with the ADMM optimizer.
    """
    self._verify_compatibility(problem)

    # debug
    self._log.debug("Initial problem: %s", problem.export_as_lp_string())

    # map integer variables to binary variables
    from ..converters.integer_to_binary import IntegerToBinary

    int2bin = IntegerToBinary()
    original_problem = problem
    problem = int2bin.convert(problem)

    # we deal with minimization in the optimizer, so turn the problem to minimization
    problem, sense = self._turn_to_minimization(problem)

    # create our computation state.
    self._state = ADMMState(problem, self._params.rho_initial)

    # parse problem and convert to an ADMM specific representation.
    self._state.binary_indices = self._get_variable_indices(
        problem, Variable.Type.BINARY
    )
    self._state.continuous_indices = self._get_variable_indices(
        problem, Variable.Type.CONTINUOUS
    )
    if self._params.warm_start:
        # warm start injection for the initial values of the variables
        self._warm_start(problem)

    # convert optimization problem to a set of matrices and vector that are used
    # at each iteration.
    self._convert_problem_representation()

    start_time = time.time()
    # we have not stated our computations yet, so elapsed time initialized as zero.
    elapsed_time = 0.0
    iteration = 0
    residual = 1.0e2

    while (iteration < self._params.maxiter and residual > self._params.tol) and (
        elapsed_time < self._params.max_time
    ):
        if self._state.step1_absolute_indices:
            op1 = self._create_step1_problem()
            self._state.x0 = self._update_x0(op1)
            # debug
            self._log.debug("Step 1 sub-problem: %s", op1.export_as_lp_string())
        # else, no binary variables exist, and no update to be done in this case.
        # debug
        self._log.debug("x0=%s", self._state.x0)

        op2 = self._create_step2_problem()
        self._state.u, self._state.z = self._update_x1(op2)
        # debug
        self._log.debug("Step 2 sub-problem: %s", op2.export_as_lp_string())
        self._log.debug("u=%s", self._state.u)
        self._log.debug("z=%s", self._state.z)

        if self._params.three_block:
            if self._state.binary_indices:
                op3 = self._create_step3_problem()
                self._state.y = self._update_y(op3)
                # debug
                self._log.debug("Step 3 sub-problem: %s", op3.export_as_lp_string())
            # debug
            self._log.debug("y=%s", self._state.y)

        self._state.lambda_mult = self._update_lambda_mult()
        # debug
        self._log.debug("lambda: %s", self._state.lambda_mult)

        cost_iterate = self._get_objective_value()
        constraint_residual = self._get_constraint_residual()
        residual, dual_residual = self._get_solution_residuals(iteration)
        merit = self._get_merit(cost_iterate, constraint_residual)
        # debug
        self._log.debug(
            "cost_iterate=%s, cr=%s, merit=%s", cost_iterate, constraint_residual, merit
        )

        # costs are saved with their original sign.
        self._state.cost_iterates.append(cost_iterate)
        self._state.residuals.append(residual)
        self._state.dual_residuals.append(dual_residual)
        self._state.cons_r.append(constraint_residual)
        self._state.merits.append(merit)
        self._state.lambdas.append(np.linalg.norm(self._state.lambda_mult))

        self._state.x0_saved.append(self._state.x0)
        self._state.u_saved.append(self._state.u)
        self._state.z_saved.append(self._state.z)
        self._state.z_saved.append(self._state.y)

        self._update_rho(residual, dual_residual)

        iteration += 1
        elapsed_time = time.time() - start_time

    binary_vars, continuous_vars, objective_value = self._get_best_merit_solution()
    solution = self._revert_solution_indexes(binary_vars, continuous_vars)

    # flip the objective sign again if required
    objective_value = objective_value * sense

    # convert back integer to binary
    base_result = OptimizationResult(
        solution, objective_value, problem.variables, OptimizationResultStatus.SUCCESS
    )
    base_result = int2bin.interpret(base_result)

    # third parameter is our internal state of computations.
    result = ADMMOptimizationResult(
        x=base_result.x,
        fval=base_result.fval,
        variables=original_problem.variables,
        state=self._state,
        status=self._get_feasibility_status(original_problem, base_result.x),
    )

    # debug
    self._log.debug(
        "solution=%s, objective=%s at iteration=%s",
        solution,
        objective_value,
        iteration,
    )
    return result