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Advances in Electrical, Electronics and Computing Technology (EECT), International Conference on

作者： Shengjie Zhang Qinggang Meng Yunshuai Zhang Xuewei Gong Xinhui Ji Zibo Power Supply Company State Grid Shandong Electric Power Company Zibo China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

With the expansion of the scale and complexity of the distribution network, the electromechanical transient simulation of the distribution network based on the traditional quasi-steady state assumption can no longer meet the current demand, and more accurate electromagnetic transient simulation calculation is needed. In order to improve the efficiency of electromagnetic transient simulation for large-scale distribution system, a new method based on the Lie Series-Padé algorithm is proposed in this paper. Firstly, the electromagnetic transient model of distribution system is established, including transformer electromagnetic transient model, distribution line electromagnetic transient model and induction motor electromagnetic transient model considering connection mode. Then, a numerical algorithm based on Lie Series-Padé approximation is proposed, which has the advantages of high precision, strong stability and explicit format, and is more suitable for large-scale electromagnetic transient simulation than the traditional implicit ladder method. Finally, a framework for electromagnetic transient simulation of distribution network system based on Lie Series-Padé algorithm is constructed, and a practical distribution network example is used to compare the proposed algorithm with the simulation results of PSCAD to verify the effectiveness of the proposed algorithm.

关键词： Couplings Fuses Distribution networks approximation algorithms Stability analysis Numerical models Computational efficiency Matrix decomposition Thermal stability Numerical stability

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Online and Streaming algorithms for Constrained k-Submodular Maximization 39

Online and Streaming Algorithms for Constrained k-Submodular...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Spaeh, Fabian Christian Ene, Alina Nguyen, Huy Department of Computer Science Boston University United States Khoury College of Computer Science Northeastern University United States

ISBN: (纸本)157735897X

Constrained k-submodular maximization is a general framework that captures many discrete optimization problems such as ad allocation, influence maximization, personalized recommendation, and many others. In many of these applications, datasets are large or decisions need to be made in an online manner, which motivates the development of efficient streaming and online algorithms. In this work, we develop single-pass streaming and online algorithms for constrained k-submodular maximization with both monotone and general (possibly non-monotone) objectives subject to cardinality and knapsack constraints. Our algorithms achieve provable constant-factor approximation guarantees which improve upon the state of the art in almost all settings. Moreover, they achieve the fastest known running times and have optimal space usage. We experimentally evaluate our algorithms on instances for ad allocation and other applications, where we observe that our algorithms are practical and scalable, and construct solutions that are comparable in value even to offline greedy algorithms. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： approximation algorithms

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Fixed Budget Bayesian Best Arm Identification in Unimodal Bandits

Fixed Budget Bayesian Best Arm Identification in Unimodal Ba...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Debamita Ghosh Manjesh Kumar Hanawal Nikola Zlatanov IITB-Monash Research Academy IIT Bombay India MLioNS Lab IEOR IIT Bombay India Innopolis University Russia

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

In high-speed vehicular communications, e.g., a high-speed train system, the beam between the base station and user is frequently misaligned, resulting in a huge amount of associated overheads. As a result, beam alignment is often challenging. This paper presents a new approach to beam alignment in millimeter wave high-speed train systems. The core idea of our approach is using Bayesian multi-armed bandit (MAB) in the beam search process, where we refer to the beams as arms. Furthermore, there exists an unimodal structure of the mean received signal strength (referred to as mean rewards) over the available beams (or arms). To this end, we develop an algorithm named Bayesian-Best Arm with Unimodality (B-BAU) that identifies the best arm with high confidence within a fixed-budget by exploiting the unimodal structure of the mean rewards over the arms. In the proposed method, by approximating the signal on each beam direction as a Gaussian random variable, for the Gaussian rewards with a Gaussian prior, we show that the expected probability that B-BAU misidentifies the optimal arm is upper bounded as $O(1/\sqrt T )$, where T is the budget. We then develop an algorithm for its frequentist counterpart named F-BAU. We demonstrate that F-BAU and B-BAU outperform the state-of-the-art algorithms through extensive simulations. While F-BAU is parameter-free, B-BAU performs better with the prior information.

关键词： Base stations Upper bound Error probability Communication systems Millimeter wave technology approximation algorithms Bayes methods Random variables Millimeter wave communication

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Streaming algorithms for Network Design

arXiv

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arXiv 2025年

作者： Chekuri, Chandra Jain, Rhea Mahabadi, Sepideh Vakilian, Ali University of Illinois at Urbana-Champaign United States Microsoft Research United States United States

We consider the Survivable Network Design problem (SNDP) in the single-pass insertion-only streaming model. The input to SNDP is an edge-weighted graph G = (V, E) and an integer connectivity requirement r(uv) for each u, v ∈ V . The objective is to find a minimum-weight subgraph H ⊆ G such that, for every pair of vertices u, v ∈ V , u and v are r(uv)-edge/vertex-connected. Recent work by Jin et al. [JKMV24] obtained approximation algorithms for edge-connectivity augmentation, and via that, also derived algorithms for edge-connectivity SNDP (EC-SNDP). In this work we consider vertex-connectivity setting (VC-SNDP) and obtain several results for it as well as improved results for EC-SNDP. • We provide a general framework for solving connectivity problems including SNDP and others in streaming;this is based on a connection to fault-tolerant spanners. For VC-SNDP, we provide an O(tk)-approximation in Õ(k1−1/tn1+1/t) space, where k is the maximum connectivity requirement, assuming an exact algorithm at the end of the stream. Using a refined LP-based analysis, we provide an O(βt)-approximation where β is the integrality gap of the natural cut-based LP relaxation. These are the first approximation algorithms in the streaming model for VC-SNDP. When applied to the EC-SNDP, our framework provides an O(t)-approximation in Õ(k1/2−1/(2t)n1+1/t + kn) space, improving the O(tlog k)-approximation of [JKMV24] using Õ(kn1+1/t) space;this also extends to element-connectivity SNDP. • We consider vertex connectivity-augmentation in the link-arrival model. The input is a k-vertex-connected spanning subgraph G, and additional weighted links L arrive in the stream;the goal is to store the minimum-weight set of links such that G∪L is (k+ 1)-vertex-connected. We obtain constant-factor approximations in near-linear space for k = 1, 2. Our result for k = 2 is based on using the SPQR tree, a novel application for this well-known representation of 2-connected graphs. Copyright © 2025, The Author

关键词： approximation algorithms

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Orthogonality is Better: Auxiliary Problems in ASO Algorithm

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Chinese Journal of Electronics 2023年第4期21卷 645-650页

作者： Taozheng ZHANG Xiaojie WANG Hui TONG Information Engineering School Communication University of China Beijing China Center for Intelligence Science and Technology Beijing University of Posts and Telecommunications Beijing China School of Science Beijing University of Posts and Telecommunications Beijing China

We propose a principle called orthogonality for Auxiliary problems (APs) selection in Alternating structure optimization (ASO) algorithm. Both theoretical analyses and experimental results indicate the following conclusions. If the weight matrices of different types of APs are orthogonal or approximately orthogonal, their multi-combinations perform better than or equal to any components. Moreover, as long as the ratios of their components are appropriate, even if the total amounts of APs are fixed, the multi-combinations still perform better than or equal to any components. In short, the principle of orthogonality holds.

关键词： Semantics Syntactics approximation algorithms Labeling Task analysis Optimization

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Energy Efficiency Optimization for Federated Learning in Cell-free Massive MIMO Systems

Energy Efficiency Optimization for Federated Learning in Cel...

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International Conference on Communications and Electronics, ICCE

作者： Xuan-Toan Dang Oh-Soon Shin School of Electronic Engineering Soongsil University Seoul Korea

ISBN: (数字)9798350379792

ISBN: (纸本)9798350379808

In recent years, federated learning (FL), a promising machine learning technique, has gained increasing attention. Unlike traditional approaches that centralize data for training a global model, FL enables model training to occur on user devices (UEs), prioritizing data privacy and security. A key challenge in implementing FL over wireless communication lies in optimizing the energy efficiency of user devices, especially considering their limited power resources. In this paper, we address the problem of minimizing total energy consumption during the training phase of FL in a cell-free massive multiple-input multiple-output system. The optimization problem is formulated with the goal of minimizing the overall energy consumption of all UEs during the FL training process. To address the nonconvex and intricate problem, we propose an iterative algorithm utilizing the inner approximation method. This approach transforms the problem into a series of successive convex problems, yielding at least one locally optimal solution. Extensive experiments show the effectiveness of the proposed algorithm in addressing the given problem. The results demonstrate the superior performance of the proposed approach compared with benchmark algorithms.

关键词： Training Wireless communication Energy consumption Federated learning Transforms Benchmark testing approximation algorithms

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Shared-Memory Parallel Dynamic Louvain Algorithm for Community Detection

Shared-Memory Parallel Dynamic Louvain Algorithm for Communi...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Subhajit Sahu Kishore Kothapalli Dip Sankar Banerjee International Institute of Information Technology Hyderabad India Hyderabad India Indian Institute of Technology Jodhpur India Karwar Rajasthan India

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Community detection refers to the identification of coherent partitions in networks. In this poster, we present a parallel dynamic Louvain algorithm that finds communities in rapidly evolving graphs. Given a batch update of edge deletions or insertions, our algorithm identifies an approximate set of affected vertices in the graph with minimal overhead and updates the community membership of each vertex. This process repeats until convergence. Our approach achieves a mean speedup of 7.3 ×, compared to our parallel and optimized implementation of Δ-screening combined with Louvain, a recently proposed state-of-the-art approach.

关键词： Distributed processing Heuristic algorithms Image edge detection Conferences approximation algorithms Partitioning algorithms Convergence

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Feature Selection based on Variable Universe Rough Set

Feature Selection based on Variable Universe Rough Set

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Wanting Wang Eric C.C. Tsang Weihua Xu Faculty of Innovation Engineering Macau University of Science and Technology Taipa Macau College of Artificial Intelligence Southwest University Chongqing PR China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

The variable universe model is an effective tool for handling uncertain knowledge. However, there remains room for improvement in the area of feature selection within this model. Addressing this gap, we propose a lower approximation reduction based on the variable universe model to enhance feature selection tasks. We design and implement corresponding algorithms, demonstrating through experimentation that our approach outperforms traditional method. Specifically, our method not only achieves rapid and effective feature selection but also significantly reduces data dimensionality. Leveraging the unique characteristics of the variable universe model, our approach accurately identifies key features within datasets, thereby enhancing efficiency and accuracy in data analysis.

关键词： Analytical models Data analysis Computational modeling Rough sets Machine learning Feature extraction approximation algorithms Data models Computational efficiency Cybernetics

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Automation of Node Redundancy for Stable Operation of Various Types of Blockchain Nodes

Automation of Node Redundancy for Stable Operation of Variou...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Kohei Ichihara Souki Aoba Masataka Morishita Advanced Technology Laboratory The Japan Research Institute Limited Tokyo Japan Digital Strategy Department Sumitomo Mitsui Banking Corporation Tokyo Japan Platform Department Ginco Inc. Tokyo Japan CTO Ginco Inc. Tokyo Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This paper describes automation of node redundancy for stable operation of many types of blockchain nodes. In recent years, traditional large companies have gradually begun to consider the use of public blockchains. When using blockchain for mission-critical purposes, it is necessary to operate a large number of nodes to increase redundancy and ensure stable operation. However, as public blockchain technology is rapidly advancing and the number of types is increasing, it is difficult for many companies to efficiently operate nodes. To overcome this difficulty, Ginco Inc. developed a system on the cloud that mainly consists of three main technologies. By automatically creating a snapshot of a volume containing chain data and attaching it to a new node, Ginco Inc. reduced the time required to start up a node. Additionally, in order to prevent requests from clients from being routed to nodes with low chain block heights, Ginco Inc. implemented a function for the load balancer to grasp the block height of each node. Furthermore, when reassigning clients to nodes due to a node failure, Ginco Inc. is able to do the reassignment evenly by using consistent hashing as the mapping algorithm. Using these automatic redundancy technologies, Ginco Inc. was able to implement node services for about 30 types of blockchains with just a few infrastructure engineers. The node services have continued to operate without any large system failure for over 5 years since its launch.

关键词： Hands Automation Redundancy Mission critical systems Companies Routing Load management approximation algorithms Blockchains Consumer electronics

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Efficient Approximate Range Aggregation over Large-scale Spatial Data Federation (Extended Abstract)

Efficient Approximate Range Aggregation over Large-scale Spa...

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38th IEEE International Conference on Data Engineering (ICDE)

作者： Shi, Yexuan Tong, Yongxin Zeng, Yuxiang Zhou, Zimu Ding, Bolin Chen, Lei Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Singapore Management Univ Sch Informat Syst Singapore Singapore

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Data federations notably increase the amount of data available for data-intensive applications such as smart mobility planning and public health emergency responses. Yet they also challenge the conventional implementation of range aggregation queries because the raw data cannot be shared within the federation and the data partition at each data silo is fixed during query processing. In this work, we propose the first-of-its-kind approximate algorithms for efficient range aggregation over spatial data federation. We devise novel singlesilo sampling algorithms that process queries in parallel and design a level sampling based algorithm which reduces the time complexity of local queries at each data silo to O(log 1 o), where o is the approximation ratio of the accuracy guarantee. Extensive experiments on real-world dataset validate the efficiency and effectiveness of the solutions.

关键词： Query processing Conferences approximation algorithms Data engineering Spatial databases Partitioning algorithms Planning

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