A Decentralised Coordination Algorithm for Maximising Sensor Coverage in Large Sensor Networks [PDF]
, 2010 In large wireless sensor networks, the problem of assigning radio frequencies to sensing agents such that no two connected sensors are assigned the same value (and will thus interfere with one another) is a major challenge.
Jennings, Nick +2 more
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Optimization beyond a single submodular function [Elektronisk resurs] : Submodular optimization for ranking, decision trees and diversity [PDF]
, 2022 Submodular functions characterize mathematically the ubiquitous ``diminishing-returns'’ property. They are widely used to describe core subjects in numerous applications, including economic utility, redundancy in information, spread of influence in ...
Gionis, Aristides, +3 more
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Submodular Optimization and Machine Learning: Theoretical Results, Unifying and Scalable Algorithms, and Applications [PDF]
, 2015 Thesis (Ph.D.)--University of Washington, 2015In this dissertation, we explore a class of unifying and scalable algorithms for a number of submodular optimization problems, and connect them to several machine learning applications.
Iyer, Rishabh Krishnan
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Cost-Oriented Mobility-Aware Caching Strategies in D2D Networks With Delay Constraint
IEEE Access, 2019 Pre-caching popular files at mobile users with the aid of device-to-device (D2D) communications can offload the data traffic to low-cost D2D links and reduce the network transmission cost.
Ruijin Sun +5 more
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Submodular Optimization over Streams with Inhomogeneous Decays
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Cardinality constrained submodular function maximization, which aims to select a subset of size at most k to maximize a monotone submodular utility function, is the key in many data mining and machine learning applications such as data summarization and maximum coverage problems.
Junzhou Zhao +4 more
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Dynamic Stepsize Techniques in DR-Submodular Maximization
MathematicsThe Diminishing-Return (DR)-submodular function maximization problem has garnered significant attention across various domains in recent years. Classic methods often employ continuous greedy or Frank–Wolfe approaches to tackle this problem; however, high
Yanfei Li, Min Li, Qian Liu, Yang Zhou
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Arithmetic Combinations of Submodular and Supermodular Optimization and Submodular Generalized Matching for Peptide Identification in Tandem Mass Spectrometry [PDF]
, 2023 Thesis (Ph.D.)--University of Washington, 2023Submodular functions have recently shown utility for a number of machine learning applications such as information gathering, document summarization, image segmentation, and string alignment, since they are ...
Bai, Wenruo
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On the complexity of submodular function minimisation on diamonds [PDF]
, 2011 Let (L: boolean AND, boolean OR) be a finite lattice and let n be a positive integer. A function f : L(n) -andgt; R is said to be submodular if f(a boolean AND b) + f(a boolean OR b) andlt;= f(a) + f(b) for all a, b is an element of L(n). In this article
Kuivinen, Fredrik,, Fredrik Kuivinen
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InformationThe occurrence of filter bubbles and echo chambers in social media recommendation systems poses a significant threat to information diversity and democratic discourse. Although graph neural networks (GNNs) achieve leading accuracy in user recommendation,
Soh Yoshida
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Submodular Function Optimization in Sensor and Social Networks II [PDF]
, 2012 Presented at the Georgia Tech Algorithms & Randomness Center workshop: Modern Aspects of Submodularity, March 19-22, 2012.Runtime: 55:01 minutes.Many applications in sensor and social networks involve discrete optimization problems.
Krause, Andreas
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