Results 11 to 20 of about 13,203 (237)
Submodular Function Maximization for Group Elevator Scheduling
, 2017 We propose a novel approach for group elevator scheduling by formulating it as the maximization of submodular function under a matroid constraint. In particular, we propose to model the total waiting time of passengers using a quadratic Boolean function.
Srikumar Ramalingam +2 more
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Maximizing Symmetric Submodular Functions [PDF]
ACM Transactions on Algorithms, 2016 Symmetric submodular functions are an important family of submodular functions capturing many interesting cases including cut functions of graphs and hypergraphs.
Feldman, Moran
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On the Reducibility of Submodular Functions [PDF]
CoRR, 2016 The scalability of submodular optimization methods is critical for their usability in practice. In this paper, we study the reducibility of submodular functions, a property that enables us to reduce the solution space of submodular optimization problems without performance loss. We introduce the concept of reducibility using marginal gains.
Jincheng Mei, Hao Zhang, Bao‐Liang Lu
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Distributed Maximization of Submodular and Approximately Submodular Functions [PDF]
2020 59th IEEE Conference on Decision and Control (CDC), 2020 We study the problem of maximizing a submodular function, subject to a cardinality constraint, with a set of agents communicating over a connected graph. We propose a distributed greedy algorithm that allows all the agents to converge to a near-optimal solution to the global maximization problem using only local information and communication with ...
Lintao Ye, Shreyas Sundaram
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Learning and Optimization with Submodular Functions [PDF]
CoRR, 2015 Tech Report - USC Computer Science CS-599, Convex and Combinatorial ...
Bharath Sankaran +4 more
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Ranking with submodular functions on a budget. [PDF]
Data Min Knowl Discov, 2022 AbstractSubmodular maximization has been the backbone of many important machine-learning problems, and has applications to viral marketing, diversification, sensor placement, and more. However, the study of maximizing submodular functions has mainly been restricted in the context of selecting a set of items.
Zhang G, Tatti N, Gionis A.
europepmc +5 more sources
Sparsification of Decomposable Submodular Functions [PDF]
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Submodular functions are at the core of many machine learning and data mining tasks. The underlying submodular functions for many of these tasks are decomposable, i.e., they are sum of several simple submodular functions. In many data intensive applications, however, the number of underlying submodular functions in the original function is so large ...
Akbar Rafiey, Yuichi Yoshida
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Misinformation Correction Maximization Problem with Edge Addition in Social Networks [PDF]
Jisuanji kexue, 2022 The popularity of online social networks such as Wechat has aroused people’s more attention to information diffusion.The spread of misinformation in social networks may lead to serious consequences,such as economic losses and public panic.Therefore ...
SONG Xin-yue, SHUAI Tian-ping, CHEN Bin
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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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Efficient Streaming Algorithms for Maximizing Monotone DR-Submodular Function on the Integer Lattice
Mathematics, 2022 In recent years, the issue of maximizing submodular functions has attracted much interest from research communities. However, most submodular functions are specified in a set function.
Bich-Ngan T. Nguyen +3 more
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