A Parallelizable Acceleration Framework for Packing Linear Programs


  • Palma London California Institute of Technology
  • Shai Vardi California Institute of Technology
  • Adam Wierman California Institute of Technology
  • Hanling Yi The Chinese University of Hong Kong


optimization, linear programs, parallel algorithms


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.




How to Cite

London, P., Vardi, S., Wierman, A., & Yi, H. (2018). A Parallelizable Acceleration Framework for Packing Linear Programs. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11778