Results 11 to 20 of about 291 (165)
CoRR, 2021 It has been well established that first order optimization methods can converge to the maximal objective value of concave functions and provide constant factor approximation guarantees for (non-convex/non-concave) continuous submodular functions. In this work, we initiate the study of the maximization of functions of the form $F(x) = G(x) +C(x)$ over a
Siddharth Mitra +2 more
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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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SIAM Journal on Discrete Mathematics, 2009 Let $f:{\cal L}\to\mathbb{R}$ be a submodular function on a modular lattice ${\cal L}$; we show that there is a maximal chain ${\cal C}$ in ${\cal L}$ on which the sequence of values of $f$ is minimal among all paths from 0 to 1 in the Hasse diagram of ${\cal L}$, in a certain well-behaved partial order on sequences of reals.
Graham R. Brightwell, Peter Winkler 0001
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On $k$-Submodular Relaxation [PDF]
SIAM Journal on Discrete Mathematics, 2016 11 pages, corrected typos, accepted in SIAM Journal on Discrete ...
Hiroshi Hirai 0001, Yuni Iwamasa
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Learning submodular functions [PDF]
Proceedings of the forty-third annual ACM symposium on Theory of computing, 2011 There has been much interest in the machine learning and algorithmic game theory communities on understanding and using submodular functions. Despite this substantial interest, little is known about their learnability from data. Motivated by applications, such as pricing goods in economics, this paper considers PAC-style learning of submodular ...
Balcan, Maria-Florina +1 more
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On additive approximate submodularity
Theoretical Computer Science, 2022 A real-valued set function is (additively) approximately submodular if it satisfies the submodularity conditions with an additive error. Approximate submodularity arises in many settings, especially in machine learning, where the function evaluation might not be exact.
Flavio Chierichetti +2 more
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Joint Video Caching and Processing for Multi-Bitrate Videos in Ultra-Dense HetNets
IEEE Open Journal of the Communications Society, 2020 Caching popular videos at the edge has been confirmed as a promising way to support low-latency video transmission and alleviate the backhaul traffic burden.
Ticao Zhang, Shiwen Mao
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International Journal of Distributed Sensor Networks, 2021 In this article, we consider the problem of optimally selecting a subset of transmitters from a transmitter set available to a multiple-input and multiple-output radar network.
Chenggang Wang +3 more
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IEEE Access, 2019 With the rapid development of real-time applications in the Internet of Things, people have paid more and more attention on the traffic delay. Cloud radio access networks (C-RANs) are seen as a novel network architecture with significant advantages in ...
Jun Zhang +5 more
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Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints
CoRR, 2013 We investigate two new optimization problems -- minimizing a submodular function subject to a submodular lower bound constraint (submodular cover) and maximizing a submodular function subject to a submodular upper bound constraint (submodular knapsack).
Rishabh K. Iyer, Jeff A. Bilmes
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