Data-Dependent Approximation in Social Computing

Weili Wu (), Yi Li (), Panos M. Pardalos () and Ding-Zhu Du ()
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Weili Wu: University of Texas at Dallas
Yi Li: University of Texas at Dallas
Panos M. Pardalos: University of Florida
Ding-Zhu Du: University of Texas at Dallas

A chapter in Approximation and Optimization, 2019, pp 27-34 from Springer

Abstract: Abstract Data-dependent approximation is a new approach for the study of nonsubmodular optimization problems. This approach has attracted a lot of research especially in the area of social computing, where nonsubmodular combinatorial optimization problems have been recently formulated. In this chapter, we present some theoretical results on the data-dependent approximation approach. In addition, some related open problems are discussed.

Date: 2019
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DOI: 10.1007/978-3-030-12767-1_3

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