# A simplicial decomposition framework for large scale convex quadratic programming

## TL;DR

This paper introduces a simplicial decomposition framework tailored for large-scale convex quadratic programming, enhancing solution efficiency and robustness through specialized strategies and techniques, with extensive empirical validation.

## Contribution

It presents novel tailored strategies for the master problem and techniques to accelerate the pricing problem within a simplicial decomposition framework for quadratic programming.

## Key findings

- Outperforms Cplex in efficiency and robustness
- Effective for real portfolio optimization problems
- Demonstrates scalability for large-scale problems

## 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.