tensorflow_probability/python/optimizer/bfgs.py
def _inv_hessian_control_inputs(inv_hessian):
  """Computes control inputs to validate a provided inverse Hessian.

  These ensure that the provided inverse Hessian is positive definite and
  symmetric.

  Args:
    inv_hessian: The starting estimate for the inverse of the Hessian at the
      initial point.

  Returns:
    A list of tf.Assert ops suitable for use with tf.control_dependencies.
  """
  # The easiest way to validate if the inverse Hessian is positive definite is
  # to compute its Cholesky decomposition.
  is_positive_definite = tf.reduce_all(
      input_tensor=tf.math.is_finite(tf.linalg.cholesky(inv_hessian)),
      axis=[-1, -2])

  # Then check that the supplied inverse Hessian is symmetric.
  is_symmetric = tf.equal(bfgs_utils.norm(
      inv_hessian - _batch_transpose(inv_hessian), dims=2), 0)

  # Simply adding a control dependencies on these results is not enough to
  # trigger them, we need to add asserts on the results.
  return [tf.Assert(is_positive_definite,
                    ['Initial inverse Hessian is not positive definite.',
                     inv_hessian]),
          tf.Assert(is_symmetric,
                    ['Initial inverse Hessian is not symmetric',
                     inv_hessian])]
def _inv_hessian_control_inputs(inv_hessian):
  """Computes control inputs to validate a provided inverse Hessian.

  These ensure that the provided inverse Hessian is positive definite and
  symmetric.

  Args:
    inv_hessian: The starting estimate for the inverse of the Hessian at the
      initial point.

  Returns:
    A list of tf.Assert ops suitable for use with tf.control_dependencies.
  """
  # The easiest way to validate if the inverse Hessian is positive definite is
  # to compute its Cholesky decomposition.
  is_positive_definite = tf.reduce_all(
      input_tensor=tf.math.is_finite(tf.linalg.cholesky(inv_hessian)),
      axis=[-1, -2])

  # Then check that the supplied inverse Hessian is symmetric.
  is_symmetric = tf.equal(bfgs_utils.norm(
      inv_hessian - _batch_transpose(inv_hessian), dims=2), 0)

  # Simply adding a control dependencies on these results is not enough to
  # trigger them, we need to add asserts on the results.
  return [tf.Assert(is_positive_definite,
                    ['Initial inverse Hessian is not positive definite.',
                     inv_hessian]),
          tf.Assert(is_symmetric,
                    ['Initial inverse Hessian is not symmetric',
                     inv_hessian])]
Computes control inputs to validate a provided inverse Hessian.

  These ensure that the provided inverse Hessian is positive definite and
  symmetric.

  Args:
    inv_hessian: The starting estimate for the inverse of the Hessian at the
      initial point.

  Returns:
    A list of tf.Assert ops suitable for use with tf.control_dependencies.