This paper presents an acceleration framework for packing linear programming problems where the amount of data available is limited, i.e., where the number of constraints m is small compared to the variable dimension n. The framework can be used as a black box to speed up linear programming solvers dramatically, by two orders of magnitude in our experiments. We present worst-case guarantees on the quality of the solution and the speedup provided by the algorithm, showing that the framework provides an approximately optimal solution while running the original solver on a much smaller problem. The framework can be used to accelerate exact solvers, approximate solvers, and parallel/distributed solvers. Further, it can be used for both linear programs and integer linear programs.
@article{arxiv.1711.06656,
title = {A Parallelizable Acceleration Framework for Packing Linear Programs},
author = {Palma London and Shai Vardi and Adam Wierman and Hanling Yi},
journal= {arXiv preprint arXiv:1711.06656},
year = {2017}
}