def d(self, p):
    '\n        Lazily estimate d_p(A) ~= || A^p ||^(1/p) where ||.|| is the 1-norm.\n        '
    if (p not in self._d):
        matvec = (lambda v: (self._a * (self._A.dot(v) - (self._mu * v))))
        rmatvec = (lambda v: (_np.conj(self._a) * (self._A.H.dot(v) - (_np.conj(self._mu) * v))))
        LO = LinearOperator(self._A.shape, dtype=self._dtype, matvec=matvec, rmatvec=rmatvec)
        est = onenormest((LO ** p))
        self._d[p] = (est ** (1.0 / p))
    return self._d[p]
Lazily estimate d_p(A) ~= || A^p ||^(1/p) where ||.|| is the 1-norm.
def d(self, p):
    '\n        \n        '
    if (p not in self._d):
        matvec = (lambda v: (self._a * (self._A.dot(v) - (self._mu * v))))
        rmatvec = (lambda v: (_np.conj(self._a) * (self._A.H.dot(v) - (_np.conj(self._mu) * v))))
        LO = LinearOperator(self._A.shape, dtype=self._dtype, matvec=matvec, rmatvec=rmatvec)
        est = onenormest((LO ** p))
        self._d[p] = (est ** (1.0 / p))
    return self._d[p]