AAAI Publications, Thirty-Second AAAI Conference on Artificial Intelligence

Font Size: 
A Parallelizable Acceleration Framework for Packing Linear Programs
Palma London, Shai Vardi, Adam Wierman, Hanling Yi

Last modified: 2018-04-29


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.


optimization; linear programs; parallel algorithms

Full Text: PDF