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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. https://resolver.caltech.edu/CaltechAUTHORS:20191010-134649754

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Abstract

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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17118PublisherArticle
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.
Funders:
Funding AgencyGrant Number
NSFCCF-1637598
NSFCNS-1518941
NSFCPS-154471
Linde Institute of Economic and Management ScienceUNSPECIFIED
International Teochew Doctors AssociationUNSPECIFIED
Subject Keywords:optimization; linear programs; parallel algorithms
Record Number:CaltechAUTHORS:20191010-134649754
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20191010-134649754
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99218
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:10 Oct 2019 21:02
Last Modified:10 Oct 2019 21:02

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