Submodular Function Optimization in Sensor and Social Networks
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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.