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+
+Quick start
+===========
+
+
+
+In the following we provide some pointers about which functions and classes 
+to use for different problems related to optimal transport (OT).
+
+
+Optimal transport and Wasserstein distance
+------------------------------------------
+
+The optimal transport problem between discrete distributions is often expressed
+as
+    .. math::
+        \gamma^* = arg\min_\gamma \sum_{i,j}\gamma_{i,j}M_{i,j}
+
+        s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0
+
+where :
+
+- :math:`M\in\mathbb{R}_+^{m\times n}` is the metric cost matrix defining the cost to move mass from bin :math:`a_i` to bin :math:`b_j`.
+- :math:`a` and :math:`b` are histograms (positive, sum to 1) that represent the weights of each samples in the source an target distributions.
+
+Solving the linear program above can be done using the function :any:`ot.emd`
+that will return the optimal transport matrix :math:`\gamma^*`:
+
+.. code:: python
+
+    # a,b are 1D histograms (sum to 1 and positive)
+    # M is the ground cost matrix
+    T=ot.emd(a,b,M) # exact linear program
+
+.. hint::
+    Examples of use for :any:`ot.emd` are available in the following examples:
+
+    - :any:`auto_examples/plot_OT_2D_samples`
+    - :any:`auto_examples/plot_OT_1D` 
+    - :any:`auto_examples/plot_OT_L1_vs_L2` 
+
+
+The value of the OT solution is often more of interest that the OT matrix :
+
+    .. math::
+        W(a,b)=\min_\gamma \sum_{i,j}\gamma_{i,j}M_{i,j}
+
+        s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0
+
+
+where :math:`W(a,b)` is the  `Wasserstein distance
+<https://en.wikipedia.org/wiki/Wasserstein_metric>`_ between distributions a and b
+It is a metrix that has nice statistical
+properties. It can computed from an already estimated OT matrix with
+:code:`np.sum(T*M)` or directly with the function :any:`ot.emd2`.
+
+.. code:: python
+
+    # a,b are 1D histograms (sum to 1 and positive)
+    # M is the ground cost matrix
+    W=ot.emd2(a,b,M) # Wasserstein distance / EMD value
+
+.. note::
+    In POT, most functions that solve OT or regularized OT problems have two
+    versions that return the OT matrix or the value of the optimal solution. Fir
+    instance :any:`ot.emd` return the OT matrix and :any:`ot.emd2` return the
+    Wassertsein distance.
+
+
+Regularized Optimal Transport
+-----------------------------
+
+Wasserstein Barycenters
+-----------------------
+
+Monge mapping and Domain adaptation with Optimal transport
+----------------------------------------
+
+
+Other applications
+------------------
+
+
+GPU acceleration
+----------------
+
+
+
+How to?
+-------
+
+
+
+1. **How to solve a discrete optimal transport problem ?**
+
+    The solver for discrete  is the function :py:mod:`ot.emd` that returns
+    the OT transport matrix. If you want to solve a regularized OT you can 
+    use :py:mod:`ot.sinkhorn`.
+
+    
+
+    Here is a simple use case:
+
+    .. code:: python
+
+       # a,b are 1D histograms (sum to 1 and positive)
+       # M is the ground cost matrix
+       T=ot.emd(a,b,M) # exact linear program
+       T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT
+
+    More detailed examples can be seen on this
+    :doc:`auto_examples/plot_OT_2D_samples`
+    
+
+2. **Compute a Wasserstein distance**
+
+
+
+