diff --git a/python/paddle/incubate/optimizer/functional/bfgs.py b/python/paddle/incubate/optimizer/functional/bfgs.py
index 23fd8dc0825f0d12743332efcb868f9c02734c54..2065b3c1c94c09cd9e820a6f4141223d685e2e16 100644
--- a/python/paddle/incubate/optimizer/functional/bfgs.py
+++ b/python/paddle/incubate/optimizer/functional/bfgs.py
@@ -49,16 +49,16 @@ def minimize_bfgs(objective_func,
         Jorge Nocedal, Stephen J. Wright, Numerical Optimization, Second Edition, 2006. pp140: Algorithm 6.1 (BFGS Method).
 
     Args:
-        objective_func: the objective function to minimize. ``objective_func`` accepts a multivariate input and returns a scalar.
-        initial_position (Tensor): the starting point of the iterates. 
+        objective_func: the objective function to minimize. ``objective_func`` accepts a 1D Tensor and returns a scalar.
+        initial_position (Tensor): the starting point of the iterates, has the same shape with the input of ``objective_func`` . 
         max_iters (int, optional): the maximum number of minimization iterations. Default value: 50.
         tolerance_grad (float, optional): terminates if the gradient norm is smaller than this. Currently gradient norm uses inf norm. Default value: 1e-7.
         tolerance_change (float, optional): terminates if the change of function value/position/parameter between two iterations is smaller than this value. Default value: 1e-9.
-        initial_inverse_hessian_estimate (Tensor, optional): the initial inverse hessian approximation at initial_position. It must be symmetric and positive definite. Default value: None.
+        initial_inverse_hessian_estimate (Tensor, optional): the initial inverse hessian approximation at initial_position. It must be symmetric and positive definite. If not given, will use an identity matrix of order N, which is size of ``initial_position`` . Default value: None.
         line_search_fn (str, optional): indicate which line search method to use, only support 'strong wolfe' right now. May support 'Hager Zhang' in the futrue. Default value: 'strong wolfe'.
         max_line_search_iters (int, optional): the maximum number of line search iterations. Default value: 50.
         initial_step_length (float, optional): step length used in first iteration of line search. different initial_step_length may cause different optimal result. For methods like Newton and quasi-Newton the initial trial step length should always be 1.0. Default value: 1.0.
-        dtype ('float32' | 'float64', optional): data type used in the algorithm. Default value: 'float32'.
+        dtype ('float32' | 'float64', optional): data type used in the algorithm, the data type of the input parameter must be consistent with the dtype. Default value: 'float32'.
         name (str, optional): Name for the operation. For more information, please refer to :ref:`api_guide_Name`. Default value: None.
 
     Returns:
diff --git a/python/paddle/incubate/optimizer/functional/lbfgs.py b/python/paddle/incubate/optimizer/functional/lbfgs.py
index f283381597733a6d6ccbd1f0048a48ca6c6fabad..e15ad56dc2d11da79827966099f99defe80003ca 100644
--- a/python/paddle/incubate/optimizer/functional/lbfgs.py
+++ b/python/paddle/incubate/optimizer/functional/lbfgs.py
@@ -50,17 +50,17 @@ def minimize_lbfgs(objective_func,
         Jorge Nocedal, Stephen J. Wright, Numerical Optimization, Second Edition, 2006. pp179: Algorithm 7.5 (L-BFGS).
 
     Args:
-        objective_func: the objective function to minimize. ``objective_func`` accepts a multivariate input and returns a scalar.
-        initial_position (Tensor): the starting point of the iterates. 
+        objective_func: the objective function to minimize. ``objective_func`` accepts a 1D Tensor and returns a scalar.
+        initial_position (Tensor): the starting point of the iterates, has the same shape with the input of ``objective_func`` . 
         history_size (Scalar): the number of stored vector pairs {si,yi}. Default value: 100.
         max_iters (int, optional): the maximum number of minimization iterations. Default value: 50.
         tolerance_grad (float, optional): terminates if the gradient norm is smaller than this. Currently gradient norm uses inf norm. Default value: 1e-7.
         tolerance_change (float, optional): terminates if the change of function value/position/parameter between two iterations is smaller than this value. Default value: 1e-9.
-        initial_inverse_hessian_estimate (Tensor, optional): the initial inverse hessian approximation at initial_position. It must be symmetric and positive definite. Default value: None.
+        initial_inverse_hessian_estimate (Tensor, optional): the initial inverse hessian approximation at initial_position. It must be symmetric and positive definite. If not given, will use an identity matrix of order N, which is size of ``initial_position`` . Default value: None.
         line_search_fn (str, optional): indicate which line search method to use, only support 'strong wolfe' right now. May support 'Hager Zhang' in the futrue. Default value: 'strong wolfe'.
         max_line_search_iters (int, optional): the maximum number of line search iterations. Default value: 50.
         initial_step_length (float, optional): step length used in first iteration of line search. different initial_step_length may cause different optimal result. For methods like Newton and quasi-Newton the initial trial step length should always be 1.0. Default value: 1.0.
-        dtype ('float32' | 'float64', optional): data type used in the algorithm. Default value: 'float32'.
+        dtype ('float32' | 'float64', optional): data type used in the algorithm, the data type of the input parameter must be consistent with the dtype. Default value: 'float32'.
         name (str, optional): Name for the operation. For more information, please refer to :ref:`api_guide_Name`. Default value: None.
 
     Returns: