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44th IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Xu, Wenzheng Xue, He Li, Ling Liang, Weifa Xu, Zichuan Zhou, Pan Jia, Xiaohua Das, Sajal K. Sichuan Univ Coll Comp Sci Chengdu Peoples R China Sichuan Fire Sci & Technol Res Inst MEM Chengdu Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Dalian Univ Technol Sch Software Dalian Peoples R China Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan Peoples R China Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

ISBN: (纸本)9798350386066;9798350386059

In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget K, the problem is to find a subset S with K nodes from a graph G so that a given submodular function f(S) on S is maximized while the induced subgraph G[S] by the nodes in S is connected, where the submodular function f can be used to model many practical application problems, such as the number of users within different service areas of the deployed UAVs in S, the sum of data rates of users served by the UAVs, the number of covered PoIs by placed sensors, etc. We then propose a novel 1-1/e/2h+2-approximation algorithm for the problem, improving the best approximation ratio 1-1/e/2h+2 for the problem so far, through estimating a novel upper bound on the problem and designing a smart graph decomposition technique, where e is the base of the natural logarithm, h is a parameter depends on the problem and its typical value is 2. In addition, when h = 2, the algorithm approximation ratio is at least 1-1/e/5 and may be as large as 1 in some special cases when K <= 21, and is no less than 1-1/e/6 when K >= 22, compared with the current best approximation ratio 1-1/e/7 (= 1-1/e/2h+3) for the problem. We finally evaluate the algorithm performance in the application of deploying a UAV network. Experimental results demonstrate the number of users within the service area of the deployed UAV network by the proposed algorithm is up to 7.5% larger than those by existing algorithms, and its empirical approximation ratio is between 0.7 and 0.99, which is close to the theoretical maximum value one.

关键词： UAV deployment sensor placement submodular function maximization approximation algorithms

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Multiexpression Symbolic Regression and Its Circuit Design Case

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第3期55卷 2250-2263页

作者： Zhang, Yu Li, Xinyue Hu, Wang Yen, Gary G. Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

Symbolic regression is commonly considered in wide-ranging applications due to its inherent capability for learning both structure and weighting parameters of an interpretable model. However, for those scenarios that require fitting multiple expressions (MEs) synchronously, existing symbolic regression algorithms need to run multiple times step by step asynchronously for fitting such a group of expressions. Due to lacking mechanisms to explicitly capture and leverage the relationships between these expressions, the coupling information among MEs will be lost in this approach. A multiexpression symbolic regression algorithm (ME-SR) is proposed in this article to address the issue in learning MEs. Additionally, a methodology for extracting the approximate maximum common subexpression (aMCSE) among these MEs is suggested to disclose the relationships. A new metric is formulated to measure the quality of an aMCSE in ME-SR by imitating the concept of intersection over union. Furthermore, an adaptive cross matrix is incorporated into the algorithm to balance the search efforts between intertask and intratask domains. The proposed ME-SR demonstrates superior performance when compared to its counterparts of single expression symbolic regression on the designed test set. Finally, the efficacy of the method is well verified by a real-world circuit design case.

关键词： Optimization Adaptation models Integrated circuit modeling Couplings Knowledge transfer Closed box Predictive models Circuit synthesis Machine learning algorithms approximation algorithms Evolutionary learning explainable machine learning maximum common subexpression (MCSE) multiexpression symbolic regression

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Deep Learning and Projection Neural Network With Finite-Time Convergence for Energy Management of Multi-Energy System

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IEEE TRANSACTIONS ON SMART GRID 2025年第3期16卷 2156-2168页

作者： Liu, Xueying He, Xing Li, Chaojie Huang, Tingwen Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China Shenzhen Univ Adv Technol Fac Comp Sci & Control Engn Shenzhen 518055 Peoples R China

In this paper, an approach based on projection neural network (PNN), sliding mode control technique, and deep learning is proposed to solve the energy management problem of multi-energy systems (MES) containing dynamic parameters. First, the sliding mode technique is introduced in the PNN to design a finite-time PNN (FTPNN). The stability and finite-time convergence of the proposed FTPNN are proved by the Lyapunov method and the setting time bound is given. Then, the deep FTPNN (DFTPNN) is designed by combining deep learning with the proposed FTPNN. The dynamic parameters in the MES that change over time are used as input variables for the DFTPNN, allowing the trained DFTPNN to respond immediately to changes in the dynamic parameters and to predict the solutions of the FTPNN with different parameters directly. Simulation experiments show that FTPNN has faster convergence compared to PNN. DFTPNN significantly reduces the computation time compared to FTPNN. DFTPNN provides predicted solutions to FTPNN. Since DFTPNN can respond immediately to changes in dynamic parameters and directly provide energy management strategies under different parameters, it can adapt to changing environments and promote the economic and stable operation of MES.

关键词： Cogeneration Heuristic algorithms Convergence Deep learning Prediction algorithms Optimization Heating systems Energy management approximation algorithms Power system stability Energy management problem multi-energy system deep learning finite-time projection neural network dynamic parameter

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Randomized Stochastic approximation algorithms 13

Randomized Stochastic Approximation Algorithms

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13th European Control Conference (ECC)

作者： Amelin, Konstantin Granichin, Oleg Granichina, Olga St Petersburg State Univ Dept Math & Mech St Petersburg 198504 Russia Russian Acad Sci Inst Problems Mech Engn Moscow 117901 Russia Herzen State Pedag Univ Russia St Petersburg Russia

ISBN: (纸本)9783952426913

Multidimensional stochastic optimization plays an important role in analysis and control of many technical systems. To solve the challenging problems of multidimensional optimization, it was suggested to use the randomized algorithms of stochastic approximation with perturbed input which have simple forms and provide consistent estimates of the unknown parameters for observations under "almost arbitrary" noise. They are easily "incorporated" in the design of quantum devices to estimate gradient vector of a multi-variable function.

关键词： approximation algorithms approximation methods Computers Convergence Noise Quantum computing Vectors approximation algorithms approximation method quantum computing Computers Noise stochastic approximation Converge Quantum devices Gradient vector randomized algorithms Unknown parameter Cloning Vectors

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Improved approximation algorithms for a Bilevel Knapsack Problem

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20th International Conference on Computing and Combinatorics (COCOON)

作者： Qiu, Xian Kern, Walter Zhejiang Univ Fac Comp Sci Hangzhou Zhejiang Peoples R China Univ Twente Dept Appl Math Twente Netherlands

ISBN: (纸本)9783319087832;9783319087825

We study the Stackelberg/bilevel knapsack problem as proposed by Chen and Zhang [4]: Consider two agents, a leader and a follower. Each has his own knapsack. (Knapsack capacities are possibly different). As usual, there is a set of items i = 1, ..., n of given weight w(i) and profits p(i). It is allowed to pack item i into both knapsacks, but in this case the corresponding profit for each player becomes p(i) + a(i), where a(i) is a given (positive or negative) number. The objective is to find a packing for the leader such that the total profit of the two knapsacks is maximized, assuming that the follower acts selfishly. We present tight approximation algorithms for all settings considered in [4].

关键词： approximation algorithms

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Improved approximation algorithms for Box Contact Representations

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22nd Annual European Symposium on algorithms (ESA) held as part of ALGO Meeting

作者： Bekos, Michael A. van Dijk, Thomas C. Fink, Martin Kindermann, Philipp Kobourov, Stephen Pupyrev, Sergey Spoerhase, Joachim Wolff, Alexander Univ Tubingen Wilhelm Schickard Inst Informat Tubingen Germany Univ Wurzburg Lehrstuhl Informat 1 Wurzburg Germany Univ Arizona Dept Comp Sci Tucson AZ 85721 USA Ural Fed Univ Inst Math & Comp Sci Ural Russia Univ Calif Santa Barbara Dept Comp Sicence Santa Barbara CA USA

ISBN: (纸本)9783662447772;9783662447765

We study the following geometric representation problem: Given a graph whose vertices correspond to axis-aligned rectangles with fixed dimensions, arrange the rectangles without overlaps in the plane such that two rectangles touch if the graph contains an edge between them. This problem is called CONTACT REPRESENTATION OF WORD NETWORKS (CROWN) since it formalizes the geometric problem behind drawing word clouds in which semantically related words are close to each other. CROWN is known to be NP-hard, and there are approximation algorithms for certain graph classes for the optimization version, MAX-CROWN, in which realizing each desired adjacency yields a certain profit. We show that the problem is APX-complete on bipartite graphs of bounded maximum degree. We present the first O(1)-approximation algorithm for the general case, when the input is a complete weighted graph, and for the bipartite case. Since the subgraph of realized adjacencies is necessarily planar, we consider several planar graph classes (stars, trees, outerplanar, and planar graphs), improving upon the known results. For some graph classes, we also describe improvements in the unweighted case, where each adjacency yields the same profit.t

关键词： approximation algorithms

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Sending Secrets Swiftly: approximation algorithms for Generalized Multicast Problems

Sending Secrets Swiftly: Approximation Algorithms for Genera...

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41st International Colloquium on Automata, Languages and Programming

作者： Nikzad, Afshin Ravi, R. Stanford Univ MS&E Dept Stanford CA 94305 USA Carnegie Mellon Univ Tepper Sch Business Pittsburgh PA 15213 USA

ISBN: (纸本)9783662439517

We consider natural generalizations of the minimum broadcast time problem under the telephone model, where a rumor from a root node must be sent via phone calls to the whole graph in the minimum number of rounds;the telephone model implies that the set of edges involved in communicating in a round form a matching. The extensions we consider involve generalizing the number of calls that a vertex may participate in (the capacitated version), allowing conference calls (the hyperedge version) as well as a new multicommodity version we introduce where the rumors are no longer from a single node but from different sources and intended for specific destinations (the multicommodity version). Based on the ideas from[6,7], we present a very simple greedy algorithm for the basic multicast problem with logarithmic performance guarantee and adapt it to the extensions to design typically polylogarithmic approximation algorithms. For the multi-commodity version, we give the first approximation algorithm with performance ratio 2(O(log log k) (root log k)) for k source-sink pairs. We provide nearly matching lower bounds for the hypercasting problem. For the multicommodity multicasting problem, we present improved guarantees for other variants involving asymmetric capacities, small number of terminals and with larger additive guarantees.

关键词： approximation algorithms graph algorithms b-matching LP rounding

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An Improved approximation Algorithm for Dynamic Minimum Linear Arrangement 41

An Improved Approximation Algorithm for Dynamic Minimum Line...

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41st International Symposium on Theoretical Aspects of Computer Science (STACS)

作者： Bienkowski, Marcin Even, Guy Univ Wroclaw Wroclaw Poland Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9783959773119

The dynamic offline linear arrangement problem deals with reordering n elements subject to a sequence of edge requests. The input consists of a sequence of m edges (i.e., unordered pairs of elements). The output is a sequence of permutations (i.e., bijective mapping of the elements to n equidistant points). In step t, the order of the elements is changed to the t-th permutation, and then the t-th request is served. The cost of the output consists of two parts per step: request cost and rearrangement cost. The former is the current distance between the endpoints of the request, while the latter is proportional to the number of adjacent element swaps required to move from one permutation to the consecutive permutation. The goal is to find a minimum cost solution. We present a deterministic O(log n log log n)-approximation algorithm for this problem, improving over a randomized O(log(2) n)-approximation by Olver et al. [22]. Our algorithm is based on first solving spreading-metric LP relaxation on a time-expanded graph, applying a tree decomposition on the basis of the LP solution, and finally converting the tree decomposition to a sequence of permutations. The techniques we employ are general and have the potential to be useful for other dynamic graph optimization problems.

关键词： Minimum Linear Arrangement dynamic Variant Optimization Problems Graph Problems approximation algorithms

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Gauss Hermite Fourier Features Based on Maximum Correntropy Criterion for Adaptive Filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年第3期72卷 1438-1451页

作者： Zhou, Tian Wang, Shiyuan Qian, Junhui Zheng, Yunfei Zhang, Qiangqiang Xiao, Yingying Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Key Lab Biopercept & Intelligent Informa Chongqing 400044 Peoples R China

Kernel adaptive filters (KAFs) are a class of nonlinear adaptive filters developed in the reproducing kernel Hilbert space, and are particularly suitable for addressing signal processing issues involving data streams and unknown nonlinearities. However, KAFs endure the issue of network structure growth as the number of training samples increases. To this end, a novel structural sparsification method for KAFs, i.e., Gauss Hermite Fourier features (GHFF) method, is first proposed by combining Gauss Hermite quadrature integration rule and Fourier transform. Subsequently, the GHFF method is integrated with the maximum correntropy criterion in filter design, leading to the development of two new adaptive filtering algorithms, i.e., GHFF maximum correntropy (GHFFMC) algorithm and stochastic batch GHFFMC (SB-GHFFMC) algorithm. The proposed GHFFMC and SB-GHFFMC algorithms are expected to exhibit excellent capabilities in characterizing the unknown nonlinear relationships within the data, along with robustness to outliers. Meanwhile, SB-GHFFMC is anticipated to exhibit superior filtering performance in comparison with GHFFMC, as it leverages a general and flexible batch gradient descent method for model optimization. Simulations on nonlinear system identification and time-series prediction of Chua's circuits confirm the performance superiorities of the proposed algorithms compared to other robust KAFs and RFF-based filters.

关键词： Kernel approximation algorithms Signal processing algorithms Polynomials Robustness Integrated circuit modeling Fourier transforms Filters Eigenvalues and eigenfunctions Adaptation models Gauss quadrature random Fourier features Gauss Hermite quadrature maximum correntropy criterion stochastic batch

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Joint Task Offloading and Resource Allocation for Integrated VLC and Sensing in Digital Twin-Aided Vehicular Edge Computing Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第4期74卷 6360-6372页

作者： Yang, Hao-Nan Wang, Jin-Yuan Zeng, Qimiao Cheng, Ming Lin, Min Wang, Jun-Bo Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210003 Peoples R China Harbin Inst Technol Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen 518055 Peoples R China Wuhan Univ Technol Hubei Key Lab Transportat Internet Things Wuhan 430070 Peoples R China China Telecom Res Inst Guangzhou 510630 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

Recently, integrated visible light communication (VLC) and sensing has emerged as a promising technology for vehicular edge computing (VEC). As the explosive increase of latency-sensitive and computation-intensive vehicular applications, limited on-board computing resources are unable to satisfy diversified requirements. This paper proposes a digital twin-aided virtualization VEC network architecture, and studies joint task offloading and resource allocation for integrated VLC and sensing. To determine the associations between channels and subtasks, we first propose a communication-sensing task offloading mechanism, where vehicles send communication and sensing data to edge servers (ESs) via the street lamps, and ESs adaptively assign subtasks for various channels. Then, we formulate a joint task offloading and resource allocation optimization problem by minimizing the overall latency. To tackle the non-convex problem, we decouple it into a communication subproblem and a sensing subproblem. We then propose a successive convex approximation and alternating minimization algorithm and an alternating minimization algorithm to solve the two subproblems, respectively. Finally, an overall algorithm framework is proposed. The convergence and complexity analysis indicates that the proposed algorithms are convergent and time-efficient. Numerical results verify the superiority of the proposed algorithms. Moreover, the effects of key parameters on latency performance are discussed.

关键词： Sensors Resource management Visible light communication Edge computing Physical layer approximation algorithms Vehicle-to-everything Minimization Radio frequency Atmospheric modeling Digital twin integrated visible light communication and sensing resource allocation task offloading vehicular edge computing

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