Python linalg.onenormest方法代码示例

# 需要导入模块: from scipy.sparse import linalg [as 别名]
# 或者: from scipy.sparse.linalg import onenormest [as 别名]
def expop_multiply_prep(op, A_1_norm=None, tol=EXPM_DEFAULT_TOL):
    """
    Returns "prepared" meta-info about operation op,
      which is assumed to be traceless (so no shift is needed).
      Used as input for use with _custom_expm_multiply_simple_core
      or fast C-reps.
    """
    assert(isinstance(op, _spsl.LinearOperator))
    if len(op.shape) != 2 or op.shape[0] != op.shape[1]:
        raise ValueError('expected op to have equal input and output dimensions')

    # n = op.shape[0]
    n0 = 1  # always act exp(op) on *single* vectors
    mu = 0  # _spsl._expm_multiply._trace(A) / float(n)
    #ASSUME op is *traceless*

    #FUTURE: get exact_1_norm specific for our ops - now just use approximate
    if A_1_norm is None:
        A_1_norm = _spsl.onenormest(op)

    #t = 1.0 # always, so t*<X> => just <X> below
    if A_1_norm == 0:
        m_star, s = 0, 1
    else:
        ell = 2
        norm_info = _spsl._expm_multiply.LazyOperatorNormInfo(op, A_1_norm=A_1_norm, ell=ell)
        m_star, s = _spsl._expm_multiply._fragment_3_1(norm_info, n0, tol, ell=ell)

    eta = 1.0  # _np.exp(t*mu / float(s)) # b/c mu always == 0 (traceless assumption)
    return mu, m_star, s, eta 

# 需要导入模块: from scipy.sparse import linalg [as 别名]
# 或者: from scipy.sparse.linalg import onenormest [as 别名]
def _onenormest_m1_power(A, p,
        t=2, itmax=5, compute_v=False, compute_w=False):
    """
    Efficiently estimate the 1-norm of (A - I)^p.

    Parameters
    ----------
    A : ndarray
        Matrix whose 1-norm of a power is to be computed.
    p : int
        Non-negative integer power.
    t : int, optional
        A positive parameter controlling the tradeoff between
        accuracy versus time and memory usage.
        Larger values take longer and use more memory
        but give more accurate output.
    itmax : int, optional
        Use at most this many iterations.
    compute_v : bool, optional
        Request a norm-maximizing linear operator input vector if True.
    compute_w : bool, optional
        Request a norm-maximizing linear operator output vector if True.

    Returns
    -------
    est : float
        An underestimate of the 1-norm of the sparse matrix.
    v : ndarray, optional
        The vector such that ||Av||_1 == est*||v||_1.
        It can be thought of as an input to the linear operator
        that gives an output with particularly large norm.
    w : ndarray, optional
        The vector Av which has relatively large 1-norm.
        It can be thought of as an output of the linear operator
        that is relatively large in norm compared to the input.

    """
    return onenormest(_MatrixM1PowerOperator(A, p),
            t=t, itmax=itmax, compute_v=compute_v, compute_w=compute_w) 

# 需要导入模块: from scipy.sparse import linalg [as 别名]
# 或者: from scipy.sparse.linalg import onenormest [as 别名]
def GetConditionNumber(self,A):
        self.matrix_condition_number = onenormest(K_b)
        return self.matrix_condition_number