Q:

Step-by-step example of solving a quadratic program with linear inequality constraints

I'm doing an exercise work about Support Vector Machines which involves solving a quadratic program of the form
$$\begin{aligned}
& \underset{\boldsymbol\alpha \in \mathbb{R}^N}{\text{minimize:}}
& & \frac{1}{2}\boldsymbol\alpha^TQ\boldsymbol\alpha +\textbf{p}^T\boldsymbol\alpha \\
& \text{subject to:}
& &  \; A\boldsymbol\alpha \geq \textbf{c},
\end{aligned}$$
where $Q$ is a positive-semidefinite matrix, $A$ a matrix and $\textbf{p}, \textbf{c}, \boldsymbol\alpha$ are vectors.  
I've been reading about quadratic programming and about the methods of solving one but I haven't found any concrete pencil-and-paper example of how to actually solve this problem with, let's say, using the conjugate gradient method.  In many textbooks I get directed into using online softwares for solving this problem but this doesn't fulfill my appetite, because I want to understand what is going on under the hood.  
Could I get a concrete example or get directed to one, where I can see or implement myself all the steps needed to solve a quadratic program?  The algorithm could be interior-point method for example.  
Thank you!  

A:

May I suggest the Frank-Wolfe method?  Here's an example found by a quick google on "Frank Wolfe example"
http://aerostudents.com/files/valueEngineeringAndOperationsOptimisation/FrankWolfeAlgorithmDemonstration.pdf

