Response to review

docs/source/functional.rst

@@ -46,7 +46,7 @@ An instance of :class:`.Functional`, ``f``, may provide three core operations.
 * Proximal operator
    - ``f.prox(v, lam)`` returns the result of the scaled proximal
      operator of ``f``, i.e., the proximal operator of ``lambda x:
-     lam * f(x)``, evaulated at the point ``v``.
+     lam * f(x)``, evaluated at the point ``v``.
    - The proximal operator of a functional :math:`f : \mathbb{R}^n \to
      \mathbb{R}` is the mapping :math:`\mathrm{prox}_f : \mathbb{R}^n
      \to \mathbb{R}^n` defined as

scico/functional/_functional.py

@@ -97,7 +97,7 @@ def prox(
             lam: Proximal parameter :math:`\lambda`.
             kwargs: Additional arguments that may be used by derived
                 classes. These include ``x0``, an initial guess for the
-                minimizer.
+                minimizer in the defintion of :math:`\mathrm{prox}`.
 
         """
         if not self.has_prox:
@@ -240,8 +240,8 @@ def prox(self, v: BlockArray, lam: float = 1.0, **kwargs) -> BlockArray:
             v: Input array :math:`\mb{v}`.
             lam: Proximal parameter :math:`\lambda`.
             kwargs: Additional arguments that may be used by derived
-                classes. These include ``x0``, an initial guess for the
-                minimizer.
+                classes.
+
         """
         if len(v.shape) == len(self.functional_list):
             return BlockArray.array([fi.prox(vi, lam) for fi, vi in zip(self.functional_list, v)])

scico/functional/_indicator.py

@@ -51,7 +51,7 @@ def prox(
         self, v: Union[JaxArray, BlockArray], lam: float = 1.0, **kwargs
     ) -> Union[JaxArray, BlockArray]:
         r"""Evaluate the scaled proximal operator of the indicator over
-            the non-negative orthant:
+            the non-negative orthant, :math:`I`,:
 
         .. math::
             [\mathrm{prox}_{\lambda I}(\mb{v})]_i =
@@ -64,8 +64,8 @@ def prox(
             v :  Input array :math:`\mb{v}`.
             lam : Proximal parameter :math:`\lambda` (has no effect).
             kwargs: Additional arguments that may be used by derived
-                classes. These include ``x0``, an initial guess for the
-                minimizer.
+                classes.
+
         """
         return snp.maximum(v, 0)
 
@@ -107,10 +107,11 @@ def prox(
         self, v: Union[JaxArray, BlockArray], lam: float = 1.0, **kwargs
     ) -> Union[JaxArray, BlockArray]:
         r"""Evalulate the scaled proximal operator of the indicator over
-        a :math:`\ell_2` ball with radius :math:`r` = `self.radius`.
+        a :math:`\ell_2` ball with radius :math:`r` = `self.radius`,
+        :math:`I`:
 
         .. math::
-            \mathrm{prox}_{\lambda I}(\mb{v}) = r \frac{\mb{v}}{\norm{\mb{v}}_2}
+            \mathrm{prox}_{\lambda I}(\mb{v}) = r \frac{\mb{v}}{\norm{\mb{v}}_2}\;.
 
         """
         return self.radius * v / norm(v)

scico/functional/_norm.py

@@ -180,8 +180,7 @@ def prox(
             v:  Input array :math:`\mb{v}`.
             lam: Proximal parameter :math:`\lambda`.
             kwargs: Additional arguments that may be used by derived
-                classes. These include ``x0``, an initial guess for the
-                minimizer.
+                classes.
 
         """
         norm_v = norm(v)
@@ -248,8 +247,8 @@ def prox(
             v:  Input array :math:`\mb{v}`.
             lam: Proximal parameter :math:`\lambda`.
             kwargs: Additional arguments that may be used by derived
-                classes. These include ``x0``, an initial guess for the
-                minimizer.
+                classes.
+
         """
 
         length = norm(v, axis=self.l2_axis, keepdims=True)