docs tweaks

docs/src/examples/c.rst

@@ -11,16 +11,16 @@ C code to solve this is below.
    :language: c
 
 After following the CMake :ref:`install instructions <c_install>`, we can
-compile the code using (assuming the library was installed in
-:code:`/usr/local/`):
+compile the code (assuming the library was installed in
+:code:`/usr/local/`) using:
 
 .. code::
 
 	  gcc -I/usr/local/include/scs -L/usr/local/lib/ -lscsdir qp.c -o qp.out
 
 .. ./qp.out > qp.c.out
 
-Then running the binary yields output
+Then running the binary yields output:
 
 .. literalinclude:: qp.c.out
    :language: none

docs/src/examples/matlab.rst

@@ -11,7 +11,7 @@ Python code to solve this is below.
    :language: matlab
 
 After following the matlab :ref:`install instructions <matlab_install>`, we can
-run the code yielding output
+run the code yielding output:
 
 .. matlab -r "run ~/git/scs/docs/src/examples/qp.m;exit"
 

docs/src/examples/python.rst

@@ -11,7 +11,7 @@ Python code to solve this is below.
    :language: python
 
 After following the python :ref:`install instructions <python_install>`, we can
-run the code yielding output
+run the code yielding output:
 
 .. python qp.py > qp.py.out
 

docs/src/examples/qp.prob

@@ -3,7 +3,7 @@ In this example we shall solve the following small quadratic program:
 
 .. math::
     \begin{array}{ll}
-    \mbox{minimize} & \frac{1}{2} x^T \begin{bmatrix}3 & -1\\ -1 & 2 \end{bmatrix}
+    \mbox{minimize} & (1/2) x^T \begin{bmatrix}3 & -1\\ -1 & 2 \end{bmatrix}
     x + \begin{bmatrix}-1 \\ -1\end{bmatrix}^T x \\
     \mbox{subject to} & \begin{bmatrix} -1 & 1\\ 1 & 0\\ 0 & 1\end{bmatrix} x \leq \begin{bmatrix}-1 \\ 0.3 \\ -0.5\end{bmatrix}
     \end{array}
@@ -12,11 +12,11 @@ over variable :math:`x \in \mathbf{R}^2`. This problem corresponds to data:
 
 .. math::
     \begin{array}{cccc}
-    P = \begin{bmatrix}3 & -1\\ -1 & 2 \end{bmatrix}, & 
-    A = \begin{bmatrix}-1 & 1\\ 1 & 0\\ 0 & 1\end{bmatrix}, &  
+    P = \begin{bmatrix}3 & -1\\ -1 & 2 \end{bmatrix}, &
+    A = \begin{bmatrix}-1 & 1\\ 1 & 0\\ 0 & 1\end{bmatrix}, &
     b = \begin{bmatrix}-1 \\ 0.3 \\ -0.5\end{bmatrix}, &
-    c = \begin{bmatrix}-1 \\ -1\end{bmatrix} 
+    c = \begin{bmatrix}-1 \\ -1\end{bmatrix}.
     \end{array}
 
-The cone :math:`\mathcal{K}` is simply the positive orthant :code:`l` of
+And the cone :math:`\mathcal{K}` is the positive orthant :code:`l` of
 dimension 3.

docs/src/index.rst

@@ -41,13 +41,13 @@ over variables
    :header-rows: 0
 
    * - :math:`x \in \mathbf{R}^n`
-     - primal variable 
-
-   * - :math:`s \in \mathbf{R}^m`
-     - slack variable 
+     - primal variable
 
    * - :math:`y \in \mathbf{R}^m`
-     - dual variable 
+     - dual variable
+
+   * - :math:`s \in \mathbf{R}^m`
+     - slack variable
 
 with data
 
@@ -56,13 +56,13 @@ with data
    :header-rows: 0
 
    * - :math:`A \in \mathbf{R}^{m \times n}`
-     - sparse data matrix
+     - sparse data matrix, see :ref:`matrices`
    * - :math:`P \in \mathbf{S}_+^{n}`
-     - sparse **symmetric positive semidefinite** matrix
+     - sparse, symmetric positive semidefinite matrix
    * - :math:`c \in \mathbf{R}^n`
-     - dense primal cost vector 
+     - dense primal cost vector
    * - :math:`b \in \mathbf{R}^m`
-     - dense dual cost vector 
+     - dense dual cost vector
    * - :math:`\mathcal{K} \subseteq \mathbf{R}^m`
      - nonempty, closed, convex cone, see :ref:`cones`
    * - :math:`\mathcal{K}^* \subseteq \mathbf{R}^m`