Approximation guarantees for parallelized maximization of monotone non-submodular function with a cardinality constraint

Emerging applications in machine learning have imposed the problem of monotone non-submodular maximization subject to a cardinality constraint. Meanwhile, parallelism is prevalent for large-scale optimization problems in bigdata scenario while adaptive complexity is an important measurement of paral...

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Bibliographic Details
Published in:	Journal of combinatorial optimization Vol. 43; no. 5; pp. 1671 - 1690
Main Authors:	Cui, Min, Xu, Dachuan, Guo, Longkun, Wu, Dan
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.07.2022 Springer Nature B.V
Subjects:	Adaptive algorithms Approximation Combinatorics Convex and Discrete Geometry Machine learning Mathematical analysis Mathematical Modeling and Industrial Mathematics Mathematics Mathematics and Statistics Maximization Operations Research/Decision Theory Optimization Theory of Computation Non-submodular optimization Submodularity ratio Parallel algorithm Cardinality constraint
ISSN:	1382-6905, 1573-2886
Online Access:	Get full text
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