{"type":"ScholarlyArticle","title":"Improved Parallel Algorithm for Non-Monotone Submodular Maximization under Knapsack Constraint","authors":[{"name":"Tan D. Tran","slug":"tan-d-tran","given":"Tan D.","family":"Tran","affiliation":"ORLab , Faculty of Computer Science , Phenikaa University , Hanoi , Vietnam"},{"name":"Canh V. Pham","slug":"canh-v-pham","given":"Canh V.","family":"Pham","affiliation":"ORLab , Faculty of Computer Science , Phenikaa University , Hanoi , Vietnam"},{"name":"Dung T. K. Ha","slug":"dung-t-k-ha","given":"Dung T.K.","family":"Ha","affiliation":"Faculty of Information Technology , VNU University of Engineering and Technology , Hanoi , Vietnam"},{"name":"Phuong N. H. Pham","slug":"phuong-n-h-pham","given":"Phuong N. H.","family":"Pham","affiliation":"Faculty of Information Technology , Ho Chi Minh City University of Industry and Trade , Ho Chi Minh , Vietnam"}],"abstract":"This work proposes an efficient  parallel algorithm for non-monotone submodular maximization under a knapsack constraint problem over the ground set of size n. Our algorithm  improves the best approximation factor of the existing parallel one from 8 to 7  with  O(log n) adaptive complexity.   The key idea of our approach is to create an alternate threshold algorithmic framework.  This new strategy alternately constructs two disjoint candidate solutions within a constant number of sequence rounds. Then, the algorithm boosts solution quality without sacrificing the adaptive complexity.  Extensive experimental studies on three applications, Revenue Maximization, Image Summarization, and Maximum Weighted Cut, show that our algorithm not only significantly increases solution quality but also requires comparative adaptivity to state-of-the-art algorithms.","doi":"10.24963/ijcai.2024/217","url":"https://ijcai.org/proceedings/2024/217","pdf":"https://ijcai.org/proceedings/2024/0217.pdf","markdown":"https://ijcai.org/proceedings/2024/217.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/217","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Constraint Satisfaction and Optimization","track":"Main Track","number":217,"pages":{"first":1961,"last":1969},"keywords":["Constraint Satisfaction and Optimization: CSO: Constraint learning and acquisition","Constraint Satisfaction and Optimization: CSO: Applications","Data Mining: DM: Big data and scalability","Constraint Satisfaction and Optimization: CSO: Constraint optimization problems"],"videos":[],"references":[{"position":1,"raw":"[Amanatidis et al., 2020] Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, and Rebecca Reif- fenhäuser. 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