Add as_cvx_operator and example

doc/source/guide/glossary.rst

@@ -76,17 +76,20 @@ Glossary
 
     proximal
         Given a closed proper convex function :math:`f`, the proximal operator is defined by
-        
+
             .. math::
-                
+
                 \text{prox}_f(v) = \arg\min_x f(x) + (1/2)||x - v||_2^2
-
+
+        proximal is also occationally used instead of ProxImaL, then refering to the proximal
+        modelling language for the solution of convex optimization problems.
+
     proximal factory
-        A proximal factory associated with a function :math:`f` is a `callable`, 
+        A proximal factory associated with a function :math:`f` is a `callable`,
         which returns the proximal of the scaled function :math:`\sigma f` when called with a
-        scalar :math:`\sigma`. This is used due to the fact that optimization methods often use 
+        scalar :math:`\sigma`. This is used due to the fact that optimization methods often use
         :math:`\text{prox}_{\sigma f}` for varying :math:`\sigma`.
-        
+
     range
         Set of elements to which an operator maps, i.e. in which the result of
         an operator evaluation lies.

doc/source/guide/in_depth/proximal_lang_guide.rst

@@ -39,7 +39,7 @@ Norms in ODL are scaled according to the underlying function space. Hence a sequ
    >>> 1 / np.sqrt(3)  # exact result
    0.577350269189
 
-this is not the case in proximal, where the norm depends on the number of discretization points. Hence a scaling that is correct for a problem in ODL needs not be correct in proximal. This also changes the definition of things like the operator norm.
+this is not the case in ProxImaL, where the norm depends on the number of discretization points. Hence a scaling that is correct for a problem in ODL needs not be correct in proximal. This also changes the definition of things like the operator norm.
 
 This also has the added effect of changing the definition of derived features, like the spectral norm of operators.
 
@@ -49,4 +49,4 @@ ODL can represent some complicated spaces, like :math:`\mathbb{R}^3 \times \math
 
    >>> space = odl.ProductSpace(odl.rn(3), odl.cn(2))
 
-This can then be used in solvers and other structures.
+This can then be used in solvers and other structures. ProxImaL currently lacks an equivalent structure.

odl/operator/oputils.py

@@ -273,8 +273,8 @@ def rmatvec(v):
 def as_proximal_lang_operator(op, norm_bound=None):
     """Wrap ``op`` as a ``proximal.BlackBox``.
 
-    This is intended to be used with the `proximal language solvers.
-    <https://github.com/comp-imaging/ProxImaL>`_
+    This is intended to be used with the `ProxImaL language solvers.
+    <https://github.com/comp-imaging/proximal>`_
 
     Parameters
     ----------
@@ -283,7 +283,7 @@ def as_proximal_lang_operator(op, norm_bound=None):
         ``shape``, and elements in these need to implement ``asarray``.
     norm_bound : float, optional
         An upper bound on the spectral norm of the operator. Note that this is
-        the norm as defined by proximal, and hence use the unweighted spaces.
+        the norm as defined by ProxImaL, and hence use the unweighted spaces.
 
     Returns
     -------