Results 101 to 110 of about 5,397,625 (216)
Ranking with submodular functions on the fly
, 2023 Maximizing submodular functions have been studied extensively for a wide range of subset-selection problems. However, much less attention has been given to the role of submodularity in sequence-selection and ranking problems. A recently-introduced framework, named \emph{maximum submodular ranking} (MSR), tackles a family of ranking problems that arise ...
Guangyi Zhang 0001 +2 more
openaire +4 more sources
IET Networks, Volume 15, Issue 1, January/December 2026.In this study, a novel approach of integrating artificial intelligence (AI) and machine learning, that is, reinforcement learning (RL), with centrality measures is introduced to improve information spreading in social networks. RL models the decision process to dynamically select powerful nodes that bypass static measures of centrality constraints ...
Mohammad Mehdi Karimi +3 more
wiley +1 more source
Data Correcting Algorithms in Combinatorial Optimization [PDF]
This paper describes data correcting algorithms. It provides the theory behind the algorithms and presents the implementation details and computational experience with these algorithms on the asymmetric traveling salesperson problem, the problem of ...
Goldengorin, Boris +2 more
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Non-Monotone DR-Submodular Function Maximization
, 2017 We consider non-monotone DR-submodular function maximization, where DR-submodularity (diminishing return submodularity) is an extension of submodularity for functions over the integer lattice based on the concept of the diminishing return property ...
Yoshida, Yuichi +3 more
core +1 more source
Online Weakly DR-Submodular Optimization Under Stochastic Cumulative Constraints
Tsinghua Science and TechnologyIn this paper, we study a class of online continuous optimization problems. At each round, the utility function is the sum of a weakly diminishing-returns (DR) submodular function and a concave function, certain cost associated with the action will occur,
Junkai Feng +3 more
doaj +1 more source
A MIN-MAX THEOREM FOR TRANSVERSAL SUBMODULAR Functions and Its Implications [PDF]
, 2013 A. Huber and V. Kolmogorov (ISCO 2012) introduced a concept of k-submodular function as a generalization of ordinary submodular (set) functions and bisubmodular functions and obtained a min-max theorem for minimization of k-submodular functions.
Tanigawa, Shin-ichi +3 more
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On the Supremum of Singleton Ratios in Submodular Functions
MathematicsLet N be a finite set of cardinality n, and let a∈N. A submodular function f on N with f(a)=1 is defined to be a-reduced if, for any decomposition f=g+h into submodular functions, where h does not depend on a, it follows that h is identically zero.
Laszlo Csirmaz
doaj +1 more source
A T2T-Based Offloading Method: Virtual Bank With Movement Prediction
IEEE Access, 2018 Rapid developments in network technologies and mobile devices improve our lives, but the resultant fast-growing traffic leads to a lack of cellular network resources and breaks the balance between profit and costs for network providers.
Lifeng Mai +3 more
doaj +1 more source
Learning and Optimization with Submodular Functions
CoRR, 2015 Tech Report - USC Computer Science CS-599, Convex and Combinatorial ...
Bharath Sankaran +4 more
openaire +2 more sources
Non-Smooth, H\"older-Smooth, and Robust Submodular Maximization [PDF]
, 2023 We study the problem of maximizing a continuous DR-submodular function that is not necessarily smooth. We prove that the continuous greedy algorithm achieves an $[(1-1/e)\OPT-\epsilon]$ guarantee when the function is monotone and H\"older-smooth, meaning
Lee, Dabeen +2 more
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