Title:
Submodular Function Optimization in Sensor and Social Networks

dc.contributor.author Krause, Andreas en_US
dc.contributor.corporatename Georgia Institute of Technology. School of Computer Science en_US
dc.contributor.corporatename Eidgenössische Technische Hochschule Zürich en_US
dc.date.accessioned 2012-04-12T18:44:30Z
dc.date.available 2012-04-12T18:44:30Z
dc.date.issued 2012-03-19
dc.description Presented at the Georgia Tech Algorithms & Randomness Center workshop: Modern Aspects of Submodularity, March 19-22, 2012. en_US
dc.description Runtime: 57:45 minutes. en_US
dc.description.abstract Many applications in sensor and social networks involve discrete optimization problems. In recent years, it was discovered that many such problems have submodular structure. These problems include optimal sensor placement, informative path planning, active learning, influence maximization, online advertising and structure learning. In contrast to most previous approaches, submodularity allows to efficiently find provably (near-)optimal solutions. In this tutorial, I will give examples of submodular optimization problems arising in sensor and social networks, discuss algorithms for solving these problems and present results on real applications. I will also discuss recent work in online and adaptive optimization of submodular functions in these domains. en_US
dc.format.extent 57:45 minutes
dc.identifier.uri http://hdl.handle.net/1853/43255
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Submodular functions en_US
dc.title Submodular Function Optimization in Sensor and Social Networks en_US
dc.type Moving Image
dc.type.genre Lecture
dspace.entity.type Publication
local.contributor.corporatename Algorithms and Randomness Center
local.contributor.corporatename College of Computing
local.relation.ispartofseries Modern Aspects of Submodularity
relation.isOrgUnitOfPublication b53238c2-abff-4a83-89ff-3e7b4e7cba3d
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isSeriesOfPublication b6799d88-f0fe-4101-9772-8f281d4bb42e
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