

def _onenormest_m1_power(A, p, t=2, itmax=5, compute_v=False, compute_w=False):
    '\n    Efficiently estimate the 1-norm of (A - I)^p.\n\n    Parameters\n    ----------\n    A : ndarray\n        Matrix whose 1-norm of a power is to be computed.\n    p : int\n        Non-negative integer power.\n    t : int, optional\n        A positive parameter controlling the tradeoff between\n        accuracy versus time and memory usage.\n        Larger values take longer and use more memory\n        but give more accurate output.\n    itmax : int, optional\n        Use at most this many iterations.\n    compute_v : bool, optional\n        Request a norm-maximizing linear operator input vector if True.\n    compute_w : bool, optional\n        Request a norm-maximizing linear operator output vector if True.\n\n    Returns\n    -------\n    est : float\n        An underestimate of the 1-norm of the sparse matrix.\n    v : ndarray, optional\n        The vector such that ||Av||_1 == est*||v||_1.\n        It can be thought of as an input to the linear operator\n        that gives an output with particularly large norm.\n    w : ndarray, optional\n        The vector Av which has relatively large 1-norm.\n        It can be thought of as an output of the linear operator\n        that is relatively large in norm compared to the input.\n\n    '
    return onenormest(_MatrixM1PowerOperator(A, p), t=t, itmax=itmax, compute_v=compute_v, compute_w=compute_w)

dataset/ETHPy150Open scipy/scipy/scipy/linalg/_matfuncs_inv_ssq.py _onenormest_m1_power/original