parameters: (A, B, its=20)
docstring: Estimate spectral norm of the difference of two matrices by the randomized
      power method.
  
      ..  This function automatically detects the matrix data type and calls the
          appropriate backend. For details, see :func:`backend.idd_diffsnorm` and
          :func:`backend.idz_diffsnorm`.
  
      Parameters
      ----------
      A : :class:`scipy.sparse.linalg.LinearOperator`
          First matrix given as a :class:`scipy.sparse.linalg.LinearOperator` with the
          `matvec` and `rmatvec` methods (to apply the matrix and its adjoint).
      B : :class:`scipy.sparse.linalg.LinearOperator`
          Second matrix given as a :class:`scipy.sparse.linalg.LinearOperator` with
          the `matvec` and `rmatvec` methods (to apply the matrix and its adjoint).
      its : int, optional
          Number of power method iterations.
  
      Returns
      -------
      float
          Spectral norm estimate of matrix difference.
docstring_summary: Estimate spectral norm of the difference of two matrices by the randomized
      power method.
docstring_tokens: Estimate spectral norm of the difference of two matrices by the randomized power method .
function: def estimate_spectral_norm_diff(A, B, its=20):
      """
      Estimate spectral norm of the difference of two matrices by the randomized
      power method.
  
      ..  This function automatically detects the matrix data type and calls the
          appropriate backend. For details, see :func:`backend.idd_diffsnorm` and
          :func:`backend.idz_diffsnorm`.
  
      Parameters
      ----------
      A : :class:`scipy.sparse.linalg.LinearOperator`
          First matrix given as a :class:`scipy.sparse.linalg.LinearOperator` with the
          `matvec` and `rmatvec` methods (to apply the matrix and its adjoint).
      B : :class:`scipy.sparse.linalg.LinearOperator`
          Second matrix given as a :class:`scipy.sparse.linalg.LinearOperator` with
          the `matvec` and `rmatvec` methods (to apply the matrix and its adjoint).
      its : int, optional
          Number of power method iterations.
  
      Returns
      -------
      float
          Spectral norm estimate of matrix difference.
      """
      from scipy.sparse.linalg import aslinearoperator
      A = aslinearoperator(A)
      B = aslinearoperator(B)
      m, n = A.shape
      matvec1 = lambda x: A. matvec(x)
      matveca1 = lambda x: A.rmatvec(x)
      matvec2 = lambda x: B. matvec(x)
      matveca2 = lambda x: B.rmatvec(x)
      if _is_real(A):
          return backend.idd_diffsnorm(
              m, n, matveca1, matveca2, matvec1, matvec2, its=its)
      else:
          return backend.idz_diffsnorm(
              m, n, matveca1, matveca2, matvec1, matvec2, its=its)
function_tokens: def estimate_spectral_norm_diff ( A , B , its = 20 ) : from scipy . sparse . linalg import aslinearoperator A = aslinearoperator ( A ) B = aslinearoperator ( B ) m , n = A . shape matvec1 = lambda x : A . matvec ( x ) matveca1 = lambda x : A . rmatvec ( x ) matvec2 = lambda x : B . matvec ( x ) matveca2 = lambda x : B . rmatvec ( x ) if _is_real ( A ) : return backend . idd_diffsnorm ( m , n , matveca1 , matveca2 , matvec1 , matvec2 , its = its ) else : return backend . idz_diffsnorm ( m , n , matveca1 , matveca2 , matvec1 , matvec2 , its = its )