Distributed Large-Scale Linear Programming
- Detailed Technology Description
- UC San Diego inventors have come up with a way to efficiently parallelize linear programming solvers using a fast-converging iterative method. The invention compares favorably against traditional conjugate-gradient methods (which are difficult to parallelize) and traditional iterative solvers (which are slow to converge to a solution). The method enables improved performance for the following industrial applications: Business Administration Product mix planningDistribution networksTruck routingStaff schedulingFinancial portfolios optimizationProfit maximizationCorporate restructuring Telecommunications Call routingNetwork designDecoding error-correcting codesInternet trafficNon-linear sampling (compressed sensing) Transportation Vehicle/packages routing (FedEx, UPS) Manufacturing VLSI chip board manufacturing Machine LearningSocial NetworksStatistical Software Packages
- Others
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Tech ID/UC Case
19876/2009-243-0
Related Cases
2009-243-0
- *Abstract
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None
- *Principal Investigator
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Name: Danny Bickson
Department:
Name: Danny Dolev
Department:
Name: Ori Shental
Department:
- Country/Region
- USA
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