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A Parallelizable Acceleration Framework for Packing Linear Programs

London, Palma and Vardi, Shai and Wierman, Adam and Yi, Hanling (2018) A Parallelizable Acceleration Framework for Packing Linear Programs. In: Thirty-Second AAAI Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence , pp. 3706-3713.

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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.

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Additional Information:© 2018 Association for the Advancement of Artificial Intelligence. This work was supported in part by NSF grants AitF-1637598, CNS-1518941, CPS-154471, the Linde Institute, and the International Teochew Doctors Association Zheng Hanming Visiting Scholar Award Scheme.
Funding AgencyGrant Number
Linde Institute of Economic and Management ScienceUNSPECIFIED
International Teochew Doctors AssociationUNSPECIFIED
Subject Keywords:optimization; linear programs; parallel algorithms
Record Number:CaltechAUTHORS:20191010-134649754
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99218
Deposited By: Tony Diaz
Deposited On:10 Oct 2019 21:02
Last Modified:10 Oct 2019 21:02

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