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Distributed Large-Scale Linear Programming

詳細技術說明: 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

*Principal Investigation: Name: Danny Bickson
Department:

Name: Danny Dolev
Department:

Name: Ori Shental
Department:

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