class_docstr: Information about an operator is lazily computed.
  
  The information includes the exact 1-norm of the operator,
  in addition to estimates of 1-norms of powers of the operator.
  This uses the notation of Computing the Action (2011).
  This class is specialized enough to probably not be of general interest
  outside of this module.
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docstr: Lazily estimate d_p(A) ~= || A^p ||^(1/p) where ||.|| is the 1-norm.
docstr_tok: ["Lazily","estimate","d_p","(","A",")","~=","||","A^p","||^","(","1\/p",")","where","||.||","is","the","1-norm","."]
code: def d(self, p):
      """
      Lazily estimate d_p(A) ~= || A^p ||^(1/p) where ||.|| is the 1-norm.
      """
      if p not in self._d:
          est = _onenormest_matrix_power(self._A, p, self._ell)
          self._d[p] = est ** (1.0 / p)
      return self._scale * self._d[p]
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  import scipy.linalg
  import scipy.sparse.linalg
  from scipy.sparse.linalg import aslinearoperator
label_desc: Use image node_id 4 for calling the LazyOperatorNormInfo obj's underlying member method code with example usage: obj.d(p) and returns: unknown