tensorflow_probability/python/optimizer/nelder_mead.py
def _shrink_towards_best(objective_function,
                         simplex,
                         best_index,
                         shrinkage,
                         batch_evaluate_objective):
  """Shrinks the simplex around the best vertex."""

  # If the contraction step fails to improve the average objective enough,
  # the simplex is shrunk towards the best vertex.
  best_vertex = simplex[best_index]
  shrunk_simplex = best_vertex + shrinkage * (simplex - best_vertex)
  objective_at_shrunk_simplex, evals = _evaluate_objective_multiple(
      objective_function,
      shrunk_simplex,
      batch_evaluate_objective)
  return (False,
          shrunk_simplex,
          objective_at_shrunk_simplex,
          evals)
def _shrink_towards_best(objective_function,
                         simplex,
                         best_index,
                         shrinkage,
                         batch_evaluate_objective):
  """Shrinks the simplex around the best vertex."""

  # If the contraction step fails to improve the average objective enough,
  # the simplex is shrunk towards the best vertex.
  best_vertex = simplex[best_index]
  shrunk_simplex = best_vertex + shrinkage * (simplex - best_vertex)
  objective_at_shrunk_simplex, evals = _evaluate_objective_multiple(
      objective_function,
      shrunk_simplex,
      batch_evaluate_objective)
  return (False,
          shrunk_simplex,
          objective_at_shrunk_simplex,
          evals)
Shrinks the simplex around the best vertex.