def garch(X: Matrix,
          kmax: int,
          momentum: float,
          start_stepsize: float,
          end_stepsize: float,
          start_vicinity: float,
          end_vicinity: float,
          sim_seed: int,
          verbose: bool):
    """
    :param X: The input Matrix to apply Arima on.
    :param kmax: Number of iterations
    :param momentum: Momentum for momentum-gradient descent (set to 0 to deactivate)
    :param start_stepsize: Initial gradient-descent stepsize
    :param end_stepsize: gradient-descent stepsize at end (linear descent)
    :param start_vicinity: proportion of randomness of restart-location for gradient descent at beginning
    :param end_vicinity: same at end (linear decay)
    :param sim_seed: seed for simulation of process on fitted coefficients
    :param verbose: verbosity, comments during fitting
    :return: 'OperationNode' containing simulated garch(1,1) process on fitted coefficients & variances of simulated fitted process & constant term of fitted process & 1-st arch-coefficient of fitted process & 1-st garch-coefficient of fitted process & drawbacks: slow convergence of optimization (sort of simulated annealing/gradient descent) 
    """
    params_dict = {'X': X, 'kmax': kmax, 'momentum': momentum, 'start_stepsize': start_stepsize, 'end_stepsize': end_stepsize, 'start_vicinity': start_vicinity, 'end_vicinity': end_vicinity, 'sim_seed': sim_seed, 'verbose': verbose}