A simplicial decomposition framework for large scale convex quadratic programming
Optimization and Control
2017-05-26 v1
Abstract
In this paper, we analyze in depth a simplicial decomposition like algorithmic framework for large scale convex quadratic programming. In particular, we first propose two tailored strategies for handling the master problem. Then, we describe a few techniques for speeding up the solution of the pricing problem. We report extensive numerical experiments on both real portfolio optimization and general quadratic programming problems, showing the efficiency and robustness of the method when compared to Cplex.
Cite
@article{arxiv.1705.09210,
title = {A simplicial decomposition framework for large scale convex quadratic programming},
author = {Enrico Bettiol and Lucas Létocart and Francesco Rinaldi and Emiliano Traversi},
journal= {arXiv preprint arXiv:1705.09210},
year = {2017}
}