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学者姓名：徐大川
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摘要 ：
The exploration of submodular optimization problems on the integer lattice offers a more precise approach to handling the dynamic interactions among repetitive elements in practical applications. In today's data-driven world, the importance of efficient and reliable privacy-preserving algorithms has become paramount for safeguarding sensitive information. In this paper, we delve into the DR-submodular and lattice submodular maximization problems subject to cardinality constraints on the integer lattice, respectively. For DR-submodular functions, we devise a differential privacy algorithm that attains a (1-1/e-rho)-approximation guarantee with additive error O(r sigma ln|N|/epsilon) for any rho>0, where N is the number of groundset, epsilon is the privacy budget, r is the cardinality constraint, and sigma is the sensitivity of a function. Our algorithm preserves O(epsilon r2)-differential privacy. Meanwhile, for lattice submodular functions, we present a differential privacy algorithm that achieves a (1-1/e-O(rho))(1-1/e-O(\rho )-approximation guarantee with additive error O(r sigma ln|N|/epsilon). We evaluate their effectiveness using instances of the combinatorial public projects problem and the budget allocation problem within the bipartite influence model.
关键词 ：
Cardinality constraint Cardinality constraint Exponential mechanism Exponential mechanism Lattice submodular Lattice submodular DR-submodular DR-submodular Differentially private Differentially private
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| GB/T 7714 | Hu, Jiaming , Xu, Dachuan , Du, Donglei et al. Differentially private submodular maximization with a cardinality constraint over the integer lattice [J]. | JOURNAL OF COMBINATORIAL OPTIMIZATION , 2024 , 47 (4) . |
| MLA | Hu, Jiaming et al. "Differentially private submodular maximization with a cardinality constraint over the integer lattice" . | JOURNAL OF COMBINATORIAL OPTIMIZATION 47 . 4 (2024) . |
| APA | Hu, Jiaming , Xu, Dachuan , Du, Donglei , Miao, Cuixia . Differentially private submodular maximization with a cardinality constraint over the integer lattice . | JOURNAL OF COMBINATORIAL OPTIMIZATION , 2024 , 47 (4) . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
This paper introduces a collaborative allocation model designed for multiple UAVs and diverse targets in maritime combat situations. The model incorporates factors such as distance, angle, interception rate, and recognition rate to comprehensively represent the UAVs' overall damage advantage against targets. Given the complexity of real -world environments and real-time demands, large-scale UAV swarm missions necessitate swift and effective responses. To address this, the paper proposes a Two -Stage Greedy Auction Algorithm, enabling the rapid and efficient completion of cooperative strike tasks within large-scale UAV swarms while preventing deadlock occurrences. In the initial allocation stage, the entropy weight method is utilized to assess task advantages, ensuring a rational allocation criterion for various metrics during the strike process. Subsequently, to enhance the overall effective strike rate within all constraints, a reassignment algorithm is designed based on effective strike benefit indices and the initial assignment result. Simulation results demonstrate the algorithm's quick and stable running time in small-scale and large-scale scenarios.
关键词 ：
Two-stage greedy auction algorithm Two-stage greedy auction algorithm UAV swarm UAV swarm Reassignment Reassignment Cooperative target allocation Cooperative target allocation
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| GB/T 7714 | Wang, Guihao , Wang, Fengmin , Wang, Jiahe et al. Collaborative target assignment problem for large-scale UAV swarm based on two-stage greedy auction algorithm [J]. | AEROSPACE SCIENCE AND TECHNOLOGY , 2024 , 149 . |
| MLA | Wang, Guihao et al. "Collaborative target assignment problem for large-scale UAV swarm based on two-stage greedy auction algorithm" . | AEROSPACE SCIENCE AND TECHNOLOGY 149 (2024) . |
| APA | Wang, Guihao , Wang, Fengmin , Wang, Jiahe , Li, Mengzhen , Gai, Ling , Xu, Dachuan . Collaborative target assignment problem for large-scale UAV swarm based on two-stage greedy auction algorithm . | AEROSPACE SCIENCE AND TECHNOLOGY , 2024 , 149 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The online optimization model was first introduced in the research of machine learning problems (Zinkevich, Proceedings of ICML, 928-936, 2003). It is a powerful framework that combines the principles of optimization with the challenges of online decision-making. The present research mainly consider the case that the reveal objective functions are convex or submodular. In this paper, we focus on the online maximization problem under a special objective function Phi(x) : [0,1](n) -> R+ which satisfies the inequality 1/2 < u(T) del(2)Phi(x), u > <= sigma center dot ||u||(1)/||x||(1) < u, del Phi(x)> for any x, u is an element of[0, 1](n), x not equal 0. This objective function is named as one sided sigma-smooth (OSS) function. We achieve two conclusions here. Firstly, under the assumption that the gradient function of OSS function is L-smooth, we propose an (1-exp((theta - 1)(theta/(1+theta))(2 sigma)))- approximation algorithm with O(root T) regret upper bound, where T is the number of rounds in the online algorithm and theta, sigma is an element of R+ are parameters. Secondly, if the gradient function of OSS function has no L-smoothness, we provide an (1 + ((theta + 1)/theta)(4 sigma))(-1)-approximation projected gradient algorithm, and prove that the regret upper bound of the algorithm is O(root T). We think that this research can provide different ideas for online non-convex and non-submodular learning.
关键词 ：
L-smooth L-smooth Online optimization Online optimization Gradient algorithm Gradient algorithm
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| GB/T 7714 | Zhang, Hongxiang , Xu, Dachuan , Gai, Ling et al. Online learning under one sided σ-smooth function [J]. | JOURNAL OF COMBINATORIAL OPTIMIZATION , 2024 , 47 (5) . |
| MLA | Zhang, Hongxiang et al. "Online learning under one sided σ-smooth function" . | JOURNAL OF COMBINATORIAL OPTIMIZATION 47 . 5 (2024) . |
| APA | Zhang, Hongxiang , Xu, Dachuan , Gai, Ling , Zhang, Zhenning . Online learning under one sided σ-smooth function . | JOURNAL OF COMBINATORIAL OPTIMIZATION , 2024 , 47 (5) . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this paper, we explore two robust models for the k-median and k-means problems: the outlier-version (k-MedO/k-MeaO) and the penalty-version (k-MedP/k-MeaP), enabling the marking and elimination of certain points as outliers. In k-MedO/k-MeaO, the count of outliers is restricted by a specified integer, while in k-MedP/k-MeaP, there's no explicit limit on outlier quantity, yet each outlier incurs a penalty cost. We introduce a novel approach to evaluate the approximation ratio of local search algorithms for these problems. This involves an adapted clustering method that captures pertinent information about outliers within both local and global optimal solutions. For k-MeaP, we enhance the best-known approximation ratio derived from local search, elevating it from 25 + epsilon to 9 + epsilon. The best-known approximation ratio for k-MedP is also obtained. Regarding k-MedO/k-MeaO, only two bi-criteria approximation algorithms based on local search exist. One violates the outlier constraint (limiting outlier count), while the other breaches the cardinality constraint (restricting the number of clusters). We focus on the former algorithm, enhancing its approximation ratios from 17+epsilon to 3+epsilon for k-MedO and from 274 + epsilon to 9 + epsilon for k-MeaO.
关键词 ：
Clustering Problems Clustering Problems Robust Robust Approximation algorithms Approximation algorithms
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| GB/T 7714 | Wu, Chenchen , Moehring, Rolf H. , Wang, Yishui et al. Approximation Algorithms for Robust Clustering Problems Using Local Search Techniques [J]. | THEORY AND APPLICATIONS OF MODELS OF COMPUTATION, TAMC 2024 , 2024 , 14637 : 197-208 . |
| MLA | Wu, Chenchen et al. "Approximation Algorithms for Robust Clustering Problems Using Local Search Techniques" . | THEORY AND APPLICATIONS OF MODELS OF COMPUTATION, TAMC 2024 14637 (2024) : 197-208 . |
| APA | Wu, Chenchen , Moehring, Rolf H. , Wang, Yishui , Xu, Dachuan , Zhang, Dongmei . Approximation Algorithms for Robust Clustering Problems Using Local Search Techniques . | THEORY AND APPLICATIONS OF MODELS OF COMPUTATION, TAMC 2024 , 2024 , 14637 , 197-208 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this paper, we consider the perfect demand matching problem (PDM) which combines aspects of the knapsack problem along with the b-matching problem. It is a generalization of the maximum weight matching problem which has been fundamental in the development of theory of computer science and operations research. This problem is NP-hard and there exists a constant c > 0 such that the problem admits no 1 + c-approximation algorithm, unless P=NP. Here, we investigate the performance of a distributed message passing algorithm called Max-sum belief propagation for computing the problem of finding the optimal perfect demand matching. As the main result, we demonstrate the rigorous theoretical analysis of the Max-sum BP algorithm for PDM, and establish that within pseudo-polynomial-time, our algorithm could converge to the optimal solution of PDM, provided that the optimal solution of its LP relaxation is unique and integral. Different from the techniques used in previous literature, our analysis is based on primal-dual complementary slackness conditions, and thus the number of iterations of the algorithm is independent of the structure of the given graph. Moreover, to the best of our knowledge, this is one of a very few instances where BP algorithm is proved correct for NP-hard problems.(c) 2023 Elsevier Inc. All rights reserved.
关键词 ：
Perfect demand matching Perfect demand matching Belief propagation Belief propagation Linear programming dual Linear programming dual Message passing Message passing Distributed algorithm Distributed algorithm
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| GB/T 7714 | Dai, Guowei , Chen, Yannan , Mao, Yaping et al. A distributed message passing algorithm for computing perfect demand matching [J]. | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2023 , 179 . |
| MLA | Dai, Guowei et al. "A distributed message passing algorithm for computing perfect demand matching" . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 179 (2023) . |
| APA | Dai, Guowei , Chen, Yannan , Mao, Yaping , Xu, Dachuan , Zhang, Xiaoyan , Zhang, Zan -Bo . A distributed message passing algorithm for computing perfect demand matching . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2023 , 179 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Many combinatorial optimization problems can be reduced to submodular optimization problems. However, many cases in practical applications do not fully comply with the diminishing returns characteristic. This paper studies the problem of maximizing weakly-monotone non-submodular non-negative set functions without constraints, and extends the normal submodular ratio to weakly-monotone submodular ratio (gamma) over cap. In this paper, two algorithms are given for the above problems. The first one is a deterministic greedy approximation algorithm, which realizes the approximation ratio of <<(gamma)over cap>/<<(gamma)over cap>+2 When (gamma) over cap = 1, the approximate ratio is 1/3, which matches the ratio of the best deterministic algorithm known for the maximization of submodular function without constraints. The second algorithm is a random greedy algorithm, which improves the approximation ratio to (gamma) over cap/(gamma) over cap +1. When (gamma) over cap = 1, the approximation ratio is 1/2, the same as the best algorithm for the maximization of unconstrained submodular set functions.
关键词 ：
Greedy Greedy Unconstrained Unconstrained Weakly-monotone Weakly-monotone Non-submodular Non-submodular
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| GB/T 7714 | Cui, Min , Du, Donglei , Xu, Dachuan et al. Two approximation algorithms formaximizing nonnegative weakly monotonic set functions [J]. | JOURNAL OF COMBINATORIAL OPTIMIZATION , 2023 , 45 (1) . |
| MLA | Cui, Min et al. "Two approximation algorithms formaximizing nonnegative weakly monotonic set functions" . | JOURNAL OF COMBINATORIAL OPTIMIZATION 45 . 1 (2023) . |
| APA | Cui, Min , Du, Donglei , Xu, Dachuan , Yang, Ruiqi . Two approximation algorithms formaximizing nonnegative weakly monotonic set functions . | JOURNAL OF COMBINATORIAL OPTIMIZATION , 2023 , 45 (1) . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Using second-order optimization methods for training deep neural networks (DNNs) has attracted many researchers. A recently proposed method, Eigenvalue-corrected Kronecker Factorization (EKFAC), proposed an interpretation by viewing natural gradient update as a diagonal method and corrects the inaccurate re-scaling factor in the KFAC eigenbasis. What's more, a new method to approximate the natural gradient called Trace-restricted Kronecker-factored Approximate Curvature (TKFAC) is also proposed, in which the Fisher information matrix (FIM) is approximated as a constant multiplied by the Kronecker product of two matrices and the traces can be kept equal before and after the approximation. In this work, we combine the ideas of these two methods and propose Trace-restricted Eigenvalue-corrected Kronecker Factorization (TEKFAC). The proposed method not only corrects the inexact re-scaling factor under the Kronecker-factored eigenbasis, but also considers the new approximation method and the effective damping technique adopted by TKFAC. We also discuss the differences and relationships among the related Kronecker-factored approximations. Empirically, our method outperforms SGD with momentum, Adam, EKFAC and TKFAC on several DNNs.
关键词 ：
Natural gradient Natural gradient Kronecker-factored approximation Kronecker-factored approximation deep neural networks deep neural networks
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| GB/T 7714 | Gao, Kaixin , Huang, Zheng-Hai , Liu, Xiaolei et al. Eigenvalue-Corrected Natural Gradient Based on a New Approximation [J]. | ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH , 2023 , 40 (01) . |
| MLA | Gao, Kaixin et al. "Eigenvalue-Corrected Natural Gradient Based on a New Approximation" . | ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 40 . 01 (2023) . |
| APA | Gao, Kaixin , Huang, Zheng-Hai , Liu, Xiaolei , Wang, Min , Wang, Shuangling , Wang, Zidong et al. Eigenvalue-Corrected Natural Gradient Based on a New Approximation . | ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH , 2023 , 40 (01) . |
| 导入链接 | NoteExpress RIS BibTex |
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| GB/T 7714 | Nong, Qingqin , Wu, Chenchen , Xu, Dachuan et al. "Machine Learning and Optimization of MLO 2021": Special Issue of Asia-Pacific Journal of Operational Research [J]. | ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH , 2023 . |
| MLA | Nong, Qingqin et al. ""Machine Learning and Optimization of MLO 2021": Special Issue of Asia-Pacific Journal of Operational Research" . | ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH (2023) . |
| APA | Nong, Qingqin , Wu, Chenchen , Xu, Dachuan , Zhang, Dongmei . "Machine Learning and Optimization of MLO 2021": Special Issue of Asia-Pacific Journal of Operational Research . | ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH , 2023 . |
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| GB/T 7714 | Cai, Xingju , Li, Min , Wang, Guanghui et al. "Special Issue on Frontiers of Operations Research of YFORSC 2020" Preface [J]. | ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH , 2021 , 38 (05) . |
| MLA | Cai, Xingju et al. ""Special Issue on Frontiers of Operations Research of YFORSC 2020" Preface" . | ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 38 . 05 (2021) . |
| APA | Cai, Xingju , Li, Min , Wang, Guanghui , Xu, Dachuan . "Special Issue on Frontiers of Operations Research of YFORSC 2020" Preface . | ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH , 2021 , 38 (05) . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
We study stable instances of the k-means problem with penalties in fixed-dimensional Euclidean space. An instance of the problem is called alpha-stable if this instance exists a sole optimal solution and the solution keeps unchanged when distances and penalty costs are scaled by a factor of no more than alpha. Stable instances of clustering problem have been used to explain why certain heuristic algorithms with poor theoretical guarantees perform quite well in practical. For any fixed epsilon > 0, we show that when using a common multi-swap local-search algorithm, a (1 + epsilon)-stable instance of the k-means problem with penalties in fixed-dimensional Euclidean space can be solved accurately in polynomial time.
关键词 ：
Local search Local search stable instance stable instance approximation algorithm approximation algorithm fixed-dimensional Euclidean space fixed-dimensional Euclidean space k-means k-means
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| GB/T 7714 | Yuan, Fan , Xu, Dachuan , Du, Donglei et al. AN EXACT ALGORITHM FOR STABLE INSTANCES OF THE k-MEANS PROBLEM WITH PENALTIES IN FIXED-DIMENSIONAL EUCLIDEAN SPACE [J]. | JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION , 2021 . |
| MLA | Yuan, Fan et al. "AN EXACT ALGORITHM FOR STABLE INSTANCES OF THE k-MEANS PROBLEM WITH PENALTIES IN FIXED-DIMENSIONAL EUCLIDEAN SPACE" . | JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION (2021) . |
| APA | Yuan, Fan , Xu, Dachuan , Du, Donglei , Li, Min . AN EXACT ALGORITHM FOR STABLE INSTANCES OF THE k-MEANS PROBLEM WITH PENALTIES IN FIXED-DIMENSIONAL EUCLIDEAN SPACE . | JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION , 2021 . |
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