Search Results - Submodular–modular algorithm

Non-submodular model for group profit maximization problem in social networks by Zhu, Jianming, Ghosh, Smita, Wu, Weili, Gao, Chuangen

“…In social networks, there exist many kinds of groups in which people may have the same interests, hobbies, or political orientation. Sometimes, group decisions…”
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Submodular + Supermodular function maximization with knapsack constraint by Shi, Majun, Yang, Zishen, Wang, Wei

“…: a greedy algorithm, an iterated submodular+modular procedure and a sandwich method. In particular, we prove that both the greedy algorithm and the iterated submodular…”
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Multiobjective Evolutionary Algorithms Are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions by Qian, Chao

“…As evolutionary algorithms (EAs) are general-purpose optimization algorithms, recent theoretical studies have tried to analyze their performance for solving general problem classes, with the goal of providing a general theoretical…”
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An accelerated deterministic algorithm for maximizing monotone submodular minus modular function with cardinality constraint by Gong, Shufang, Liu, Bin, Fang, Qizhi

“… For submodular maximization problems with constraints, some work has been done including the design of approximation algorithms, the measurement of approximation algorithms in terms of quality and efficiency, etc…”
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Algorithms for maximizing monotone submodular function minus modular function under noise by Gong, Shufang, Liu, Bin, Geng, Mengxue, Fang, Qizhi

“…Submodular function has the property of diminishing marginal gain, and thus it has a wide range of applications in combinatorial optimization and in emerging…”
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Multi-objective Evolutionary Algorithms are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions by Qian, Chao

“…As evolutionary algorithms (EAs) are general-purpose optimization algorithms, recent theoretical studies have tried to analyze their performance for solving general problem classes, with the goal of providing a general theoretical…”
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Re-ranking Based Diversification: A Unifying View by Shameem A Puthiya Parambath

“…We analyze different re-ranking algorithms for diversification and show that majority of them are based on maximizing submodular/modular functions from the class of parameterized concave/linear over modular functions…”
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Multi-objective Evolutionary Algorithms are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions by Chao, Qian

“…As evolutionary algorithms (EAs) are general-purpose optimization algorithms, recent theoretical studies have tried to analyze their performance for solving general problem classes, with the goal of providing a general theoretical…”
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Effector Detection Problem in Social Networks by Zhang, Yapu, Yang, Wenguo, Wu, Weili, Li, Yi

“… Thus, we address this problem through the modular-modular algorithm. For the constrained…”
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Regularized nonmonotone submodular maximization by Lu, Cheng, Yang, Wenguo, Gao, Suixiang

“… The main technique we use is to introduce a distorted objective function, which varies weights of the submodular component g and the modular component ℓ…”
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A Compact Representation for Modular Semilattices and Its Applications by Hiroshi Hirai, So Nakashima

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Lattice polyhedra and submodular flows by Fujishige, Satoru, Peis, Britta

“… Thus, submodular flow algorithms can be applied to this class of lattice polyhedra. In contrast to a previous result of Schrijver, we do not explicitly require that the lattice is distributive. Moreover, our reduction is very simple in that it only uses an arbitrary maximal chain in the lattice…”
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Practical Parallel Algorithms for Non-Monotone Submodular Maximization by Cui, Shuang, Han, Kai, Tang, Jing, Li, Xueying, Zhiyuli, Aakas, Li, Hanxiao

“… With the increasing size of datasets in these domains, there is a pressing need to develop efficient and parallelizable algorithms for submodular maximization…”
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A Compact Representation for Modular Semilattices and Its Applications by Hirai, Hiroshi, Nakashima, So

“…A modular semilattice is a semilattice generalization of a modular lattice. We establish a Birkhoff-type representation theorem for modular semilattices, which…”
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Two-stage submodular maximization problem beyond nonnegative and monotone by Liu, Zhicheng, Chang, Hong, Ma, Ran, Du, Donglei, Zhang, Xiaoyan

“… submodular function and a nonnegative monotone modular function. We give two bi-factor approximation algorithms for this problem…”
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Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity by Jang-Hyun, Kim, Choo, Wonho, Jeong, Hosan, Hyun Oh Song

“…While deep neural networks show great performance on fitting to the training distribution, improving the networks' generalization performance to the test…”
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Constrained Submodular Optimization and Applications in Information Diffusion by Padmanabhan, Madhavan Rajagopal

“… We provide efficient approximation algorithms with data-dependent additive errors. We focus on an application in Information Diffusion by formulating the Constrained Influence Maximization Problem…”
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Regularized Unconstrained Weakly Submodular Maximization by Zhu, Yanhui, Basu, Samik, Pavan, A

“…\) is a monotone, non-negative, weakly submodular set function and \(c\) is a modular function. We design a deterministic approximation algorithm that runs with \({{O}}(\frac{n}{\epsilon}\log \frac{n}{\gamma \epsilon…”
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Computing the nc-rank via discrete convex optimization on CAT(0) spaces by Hamada, Masaki, Hirai, Hiroshi

“…}\). Our algorithm is based on a combination of submodular optimization on modular lattices and convex optimization on CAT(0) spaces…”
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Submodular Optimization With Constraints and Fairness: Algorithmic Advances and Applications by Zhu, Yanhui

“… We develop efficient approximation algorithms with provable guarantees, employing evolutionary, deterministic, and randomized…”
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