t3toolbox.uniform_tucker_tensor_train.ut3_sub
=============================================

.. py:function:: t3toolbox.uniform_tucker_tensor_train.ut3_sub(x: UniformTuckerTensorTrain, y: UniformTuckerTensorTrain, squash: bool = True, use_jax: bool = False) -> UniformTuckerTensorTrain

   Subtract two UniformTuckerTensorTrains, x,y -> x-y.

   :param x_cores: First summand cores
   :type x_cores: UniformTuckerTensorTrainCores
   :param x_masks: First summand masks
   :type x_masks: UniformTuckerTensorTrainMasks
   :param y_cores: Second summand cores
   :type y_cores: UniformTuckerTensorTrainCores
   :param y_masks: Second summand masks
   :type y_masks: UniformTuckerTensorTrainMasks
   :param xnp: Linear algebra backend. Default: np (numpy)

   :returns: * *UniformTuckerTensorTrainCores* -- Cores for sum, x+y
             * *UniformTuckerTensorTrainMasks* -- Cores for sum x+y

   .. rubric:: Examples

   >>> import numpy as np
   >>> import t3toolbox.tucker_tensor_train as t3
   >>> import t3toolbox.uniform_tucker_tensor_train as ut3
   >>> x = t3.t3_corewise_randn((14,15,16), (4,6,5), (2,3,2,2), stack_shape=(2,3))
   >>> ux = ut3.t3_to_ut3(x)
   >>> y = t3.t3_corewise_randn((14,15,16), (6,7,8), (3,5,6,1), stack_shape=(2,3))
   >>> uy = ut3.t3_to_ut3(y)
   >>> ux_minus_uy = ut3.ut3_sub(ux, uy)
   >>> print(np.linalg.norm(x.to_dense() - y.to_dense() - ux_minus_uy.to_dense()))
   1.7975763647128273e-12