Solving Linearly Constrained Quadratic Programming with Coordinate Descent Does anybody have any idea about how to solve the following problem with Coordinate Descent?
\begin{align}
\min &\quad \mathbf{x}^{\top}P\mathbf{x} + b^{\top}\mathbf{x}\\
\text{Subject to}& \quad A\mathbf{x} \leq c
\end{align}
I don't want to use the Bump Function technique and need the Coordinate Descent to be able to solve it in high dimensions.

assuming $P$ to be positive semi-definite, your problem is simply a standard QP with linear constraints. Unless it shows some peculiarities in the linear constraints, the structure of $P$ or a huge dimension, I would just pick up any established QP solver (MOSEK, CPLEX, Gurobi...) or even just quadprog in MATLAB. 
The papers you refer to deals to some specific situations:


*

*Tseng et al. considers non-smooth functions and this is not your case.

*Lin et al. is particularly focused on SVN application which are really huge problems. But in that case they solve the dual which a box-constrained QP which is somehow easier.
In general I would search for QP algorithms well before implement your own.
