Results 31 to 40 of about 1,352 (208)
Continuous Submodular Maximization: Beyond DR-Submodularity
CoRR, 2020 19 ...
Moran Feldman, Amin Karbasi
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Approximate Submodularity and Its Implications in Discrete Optimization [PDF]
, 2022 Submodularity is a key property in discrete optimization. Submodularity has been widely used for analyzing the greedy algorithm to give performance bounds and providing insight into the construction of valid inequalities for mixed-integer programs.
Ajayi, Temitayo +2 more
core
Submodularity Gaps for Selected Network Design and Matching Problems [PDF]
, 2023 Submodularity in combinatorial optimization has been a topic of many studies and various algorithmic techniques exploiting submodularity of a studied problem have been proposed.
Lewandowski, Mateusz +3 more
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Through the lens of sequence submodularity [PDF]
, 2020 Several real-world problems in engineering and applied science require the selection of sequences that maximize a given reward function. Optimizing over sequences as opposed to sets requires exploring an exponentially larger search space and can become ...
Fagnani F., Bernardini S., Piacentini C.
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CoRR, 2018 This short article aims at demonstrate that the Intersection over Union (or Jaccard index) is not a submodular function. This mistake has been made in an article which is cited and used as a foundation in another article. The Intersection of Union is widely used in machine learning as a cost function especially for imbalance data and semantic ...
Tanguy Kerdoncuff, Rémi Emonet
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Greedy approximations for minimum submodular cover with submodular cost [PDF]
Computational Optimization and Applications, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Peng-Jun Wan +3 more
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Entropy, 2018 The ability to integrate information in the brain is considered to be an essential property for cognition and consciousness. Integrated Information Theory (IIT) hypothesizes that the amount of integrated information ( Φ ) in the brain is related to ...
Jun Kitazono +2 more
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Improved algorithms for submodular function minimization and submodular flow [PDF]
Proceedings of the thirty-second annual ACM symposium on Theory of computing, 2000 Very recently, two groups of researchers independently developed the first combinatorial, strongly polynomial-time algorithms for submodular function minimization (Iwata, Fleischer, Fujishige; and Schrijver). In this paper, we improve on these algorithms and show that the ideas generated in the design of these algorithms are helpful in other contexts ...
Lisa Fleischer, Satoru Iwata 0001
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On Submodular Contextual Bandits
CoRR, 2021 We consider the problem of contextual bandits where actions are subsets of a ground set and mean rewards are modeled by an unknown monotone submodular function that belongs to a class $\mathcal{F}$. We allow time-varying matroid constraints to be placed on the feasible sets.
Dean P. Foster, Alexander Rakhlin
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Regularized Submodular Maximization With a
IEEE Access, 2023 With the development of the Internet and the emergence of various social-media platforms, designing approximation algorithms for optimization problems such as the influence maximization in social networks has received widespread attention.
Qingqin Nong, Zhijia Guo, Suning Gong
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