Authors: Ion Necoară, George Flurche
The 2-RCD code is able to solve huge structured convex
quadratic programs with a single linear equality
constraints and box constraints using the algorithm 2-RCD
developed in the paper: I. Necoara, A. Patrascu, A random
coordinate descent algorithm for optimization problems with
composite objective function and linear coupled constraints,
Computational Optim. & Applications, 57(2): 307-337, 2014.
2-RCD algorithm solves the following problem:
min (0.5*transpose(x)*Q*x + transpose(q)*x), with:
transpose(a)*x=b and x is in range [l, u]
The algorithm is implemented in both Python and Matlab
programming languages and it was validated by a comparison
with the modeling system for convex optimization- cvx
Hence, to analyse the performance of the algorithm, we
trained it with data provided by libsvm and performed the same
classification using both 2-RCD and svm and compared the
results