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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Valery P. Mochalov Natal'ya Yu. Bratchenko Dar'ya V. Gosteva Department of Digital Robotic Systems and Electronics Institute of Advanced Engineering North-Caucasus Federal University Stavropol Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The article considers the development of a dynamic algorithm for distributing and balancing the load, characterized by nonlinear parameters and fractal structure, between the computing resources of cloud data centers (DCs). A rationale is given for the application of methods of nonlinear dynamics and chaos theory to solving this problem, which is based on the topological similarity of the behavior of deterministic chaotic systems and the input network load of the data center. The load randomness test was conducted by calculating Lyapunov indices. To eliminate noise components of network traffic, the principal component method of its time series was applied. The problem of phase space reconstruction is solved by the delay method based on the embedding theorem and the average mutual information method. Based on this reconstruction, a method for predicting the load dynamics was developed. It was presented in the form of a system of discrete mappings connected by an algebraic polynomial, providing successful prediction of irregular time series and forming the basis for constructing a matrix for distributing data center resources. The aim of the article is to increase the efficiency of using the resources of data center cluster servers in conditions of a fractal structure of the network load by developing a dynamic algorithm for its distribution and balancing based on nonlinear dynamics methods.

关键词： Data centers Heuristic algorithms Time series analysis Clustering algorithms Telecommunication traffic approximation algorithms Dynamic scheduling Polynomials Nonlinear dynamical systems Servers

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Max-Min Diversification with Asymmetric Distances

arXiv

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arXiv 2025年

作者： Kumpulainen, Iiro Adriaens, Florian Tatti, Nikolaj University of Helsinki Helsinki Finland University of Helsinki HIIT Helsinki Finland

One of the most well-known and simplest models for diversity maximization is the Max-Min Diversification (MMD) model, which has been extensively studied in the data mining and database literature. In this paper, we initiate the study of the Asymmetric Max-Min Diversification (AMMD) problem. The input is a positive integer k and a complete digraph over n vertices, together with a nonnegative distance function over the edges obeying the directed triangle inequality. The objective is to select a set of k vertices, which maximizes the smallest pairwise distance between them. AMMD reduces to the well-studied MMD problem in case the distances are symmetric, and has natural applications to query result diversification, web search, and facility location problems. Although the MMD problem admits a simple 12 -approximation by greedily selecting the next-furthest point, this strategy fails for AMMD and it remained unclear how to design good approximation algorithms for AMMD. We propose a combinatorial 61k -approximation algorithm for AMMD by leveraging connections with the Maximum Antichain problem. We discuss several ways of speeding up the algorithm and compare its performance against heuristic baselines on real-life and synthetic datasets. © 2025, CC BY.

关键词： approximation algorithms

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Multi-Block UAMP Detection for AFDM Under Fractional Delay-Doppler Channel

Multi-Block UAMP Detection for AFDM Under Fractional Delay-D...

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IEEE Conference on Wireless Communications and Networking

作者： Jin Xu Zijian Liang Kai Niu Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Affine Frequency Division Multiplexing (AFDM) is considered as a promising solution for next-generation wireless systems due to its satisfactory performance in high-mobility scenarios. By adjusting AFDM parameters to match the multi-path delay and Doppler shift, AFDM can achieve two-dimensional time-frequency diversity gain. However, under fractional delay-Doppler channels, AFDM encounters energy dispersion in the affine domain, which poses significant challenges for signal detection. This paper first investigates the AFDM system model under fractional delay-Doppler channels. To address the energy dispersion in the affine domain, a unitary transformation based approximate message passing (UAMP) algorithm is proposed. The algorithm performs unitary transformations and message passing in the time domain to avoid the energy dispersion issue. Additionally, we implemented block-wise processing to reduce computational complexity. Finally, the empirical extrinsic information transfer (E-EXIT) chart is used to evaluate iterative detection performance. Simulation results show that UAMP significantly outperforms Gaussian approximate message passing (GAMP) under fractional delay-Doppler conditions.

关键词： Wireless communication Time-frequency analysis Message passing Simulation approximation algorithms Performance analysis Time-domain analysis Dispersion Signal detection Next generation networking

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Spectral Clustering and Multi-Modal Stable Matching for Enhanced Management of Photovoltaic Stations and Virtual Power Plants

Spectral Clustering and Multi-Modal Stable Matching for Enha...

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International Conference on Green Energy and Applications (ICGEA)

作者： Yang Yang Boon Han Lim Yicheng Xu Yong Zhang Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenzhen China Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang Malaysia Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen University of Advanced Technology Shenzhen China

ISBN: (数字)9798331530778

ISBN: (纸本)9798331530785

As renewable energy sources like photovoltaic (PV) stations expand, efficient management and operation are becoming increasingly important for maintaining grid stability and optimizing resource allocation. In this paper, we introduce a novel framework that integrates spectral clustering and multi-modal stable matching to enhance the coordination of PV stations and Virtual Power Plants (VPPs). We begin by applying spectral clustering to group PV stations based on geographic locations and key weather conditions. Our custom-built similarity matrix for PV stations, combined with our theoretical insights, suggests that using fewer eigenvectors in spectral clustering not only reduces computation time but also improves clustering accuracy. This method ensures that each cluster of PV stations shares similar environmental conditions, allowing for more effective collective management. In the second stage, we utilize multi-modal stable matching to assign these PV clusters to VPPs. Unlike traditional stable matching, the multi-modal approach considers multiple factors, such as energy demand, geographic proximity, and VPP capacity, resulting in a more balanced and stable allocation. We validate our approach through experiments on both synthetic and real-world PV station data. The results demonstrate that our framework leads to more accurate grouping of PV stations, facilitating more effective decision-making across multiple criteria. This also enhances work offloading when tasks become intensive on certain nodes. Our work presents a promising solution for managing large-scale renewable energy resources in future power systems, improving both efficiency and stability.

关键词： Photovoltaic systems Energy consumption Stability criteria Clustering algorithms Virtual power plants Power system stability approximation algorithms Resource management Thermal stability Meteorology

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Energy Efficiency Optimization for RIS-Aided Systems Using a Measured Power Model

Energy Efficiency Optimization for RIS-Aided Systems Using a...

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IEEE Conference on Wireless Communications and Networking

作者： Shuwen Lin Jian Sang Xiao Li Wankai Tang Marco Di Renzo Shi Jin National Mobile Communications Research Laboratory Southeast University Nanjing China CNRS CentraleSupélec Laboratoire des Signaux et Systèmes Université Paris-Saclay Gif-sur-Yvette France Department of Engineering King's College London Centre for Telecommunications Research London United Kingdom

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Reconfigurable intelligent surface (RIS) technology has gained wide recognition for its ability of enhancing the performance of wireless communication systems. This paper investigates the energy efficiency (EE) of a discrete phase shift RIS-assisted multi-cell communication system. Assuming that only statistical channel state information is available, we maximize the EE under a practical measured-based RIS power consumption model. Approximating the discrete measured-based RIS power consumption model by a continuous differentiable function, the problem is addressed by jointly optimizing the transmit beamforming at the base stations and the RIS phase shift matrix, by using fractional programming and complex circle manifold techniques. Simulation results demonstrate that the proposed algorithm can effectively achieve near-optimal EE performance and low RIS power consumption.

关键词： Wireless communication Power demand Power measurement Array signal processing Simulation Reconfigurable intelligent surfaces approximation algorithms Energy efficiency Optimization Channel state information

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Proportionally Fair Matching via Randomized Rounding 39

Proportionally Fair Matching via Randomized Rounding

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Duppala, Sharmila Grammel, Nathaniel Luque, Juan MacRury, Calum Srinivasan, Aravind Department of Computer Science University of Maryland College Park United States Graduate School of Business Columbia University United States

ISBN: (纸本)157735897X

Given an edge-colored graph, the goal of the proportional fair matching problem is to find a maximum weight matching while ensuring proportional representation (with respect to the number of edges) of each color. The colors may correspond to demographic groups or other protected traits where we seek to ensure roughly equal representation from each group. It is known that, assuming ETH, it is impossible to approximate the problem with colors in time 2o()nO(1) (i.e., subexponential in ) even on unweighted path graphs. Further, even determining the existence of a non-empty matching satisfying proportionality is NP-Hard. To overcome this hardness, we relax the stringent proportional fairness constraints to a probabilistic notion. We introduce a notion we call δ-PROBABLYFAIR, where we ensure proportionality up to a factor of at most (1 ± δ) for some small δ > 0 with high probability. The violation δ can be brought arbitrarily close to 0 for some good instances with large values of matching size. We propose and analyze simple and fast algorithms for bipartite graphs that achieve constant-factor approximation guarantees, and return a δ-PROBABLYFAIR matching. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： approximation algorithms

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Energy-Efficient Beamforming Based on Statistical CSI for Multicast LEO Satellite Communications

Energy-Efficient Beamforming Based on Statistical CSI for Mu...

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IEEE Conference on Wireless Communications and Networking

作者： Shiang-Yu Kung Hung-Yun Hsieh Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Low Earth Orbit (LEO) satellites have recently attracted attention as solutions for enabling global connectivity because of the low propagation latency and low path loss compared to other satellite systems. In the meantime, however, the high orbital speed as well as the low production cost have rendered power consumption minimization as one of the most critical issues for performance optimization in LEO satellite communications. In this paper, we investigate energy-efficient beamforming design for LEO satellites that support multigroup multicast downlink communications. Different from related work that assumes availability of instantaneous channel state information (CSI), our design relies only on the slow-varying statistical CSI (sCSI). The formulated problem aims to maximize energy efficiency while meeting the target data rate requirements. To solve the problem, we first approximate the average rate function by applying a closed-form tight upper bound. We then use the Dinkelbach's algorithm to transform the fractional function for energy efficiency into a concave function. We finally adopt the Convex-Concave Procedure (CCCP) to solve the problem. Numerical results show that the proposed algorithm is able to achieve higher energy efficiency than existing approaches for multicast LEO satellite communications.

关键词： Satellites Multicast algorithms Upper bound Array signal processing Low earth orbit satellites Transforms Production approximation algorithms Propagation losses Energy efficiency

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Large Covariance Matrix Estimation for Groups of Highly Correlated Variables via Nonconvex Optimization

Large Covariance Matrix Estimation for Groups of Highly Corr...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shanshan Zou Ziping Zhao School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper addresses the problem of covariance matrix estimation in scenarios where the underlying variables can be divided into groups, with variables within each group being highly correlated. Consequently, the covariance matrix displays both sparse and approximately low-rank characteristics due to these highly correlated groups. By appropriately rearranging the variables, the covariance matrix can be transformed into an approximately block diagonal form. In this work, we investigate the estimation of covariance matrices under this structure in high dimensions. We propose a least squares-based covariance estimation method that incorporates a trace norm along with a nonconvex sparsity regularizer to promote both low-rankness and sparsity. Additionally, we introduce a spectral constraint to ensure the positive semi-definiteness of the covariance matrix, even in cases of finite samples, while permitting the integration of prior spectral information. To solve this nonconvex statistical estimation problem, we develop an algorithm based on the majorization-minimization framework, which iteratively solves a convex subproblem. We provide theoretical guarantees that demonstrate the proposed algorithm converges to an estimator achieving the oracle statistical rate under mild technical conditions. Numerical experiments corroborate these theoretical findings.

关键词： Estimation Signal processing algorithms Clustering algorithms Signal processing approximation algorithms Acoustics Covariance matrices Speech processing Optimization Least mean squares methods

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OneBatchPAM: A Fast and Frugal K-Medoids Algorithm 39

OneBatchPAM: A Fast and Frugal K-Medoids Algorithm

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： de Mathelin, Antoine Cecchi, Nicolas Enrique Deheeger, François Mougeot, Mathilde Vayatis, Nicolas Centre Borelli Université Paris-Saclay CNRS ENS Paris-Saclay France Michelin France

ISBN: (纸本)157735897X

This paper proposes a novel k-medoids approximation algorithm to handle large-scale datasets with reasonable computational time and memory complexity. We develop a local-search algorithm that iteratively improves the medoid selection based on the estimation of the k-medoids objective. A single batch of size m n provides the estimation, which reduces the required memory size and the number of pairwise dissimilarities computations to O(mn), instead of O(n2) compared to most k-medoids baselines. We obtain theoretical results highlighting that a batch of size m = O(log(n)) is sufficient to guarantee, with strong probability, the same performance as the original local-search algorithm. Multiple experiments conducted on real datasets of various sizes and dimensions show that our algorithm provides similar performances as state-of-the-art methods such as FasterPAM and BanditPAM++ with a drastically reduced running time. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： approximation algorithms

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Perfect Sampling in Turnstile Streams Beyond Small Moments

arXiv

引用

arXiv 2025年

作者： Woodruff, David P. Xie, Shenghao Zhou, Samson Carnegie Mellon University Google Research United States Texas A&M University United States

Given a vector x ∈ Rn induced by a turnstile stream S, a non-negative function G : R → R, a perfect G-sampler outputs an index i with probability[Formula presented]Jayaram and Woodruff (FOCS 2018) introduced a perfect Lp-sampler, where G(z) = |z|p, for p ∈ (0, 2]. In this paper, we solve this problem for p > 2 by a sampling-and-rejection method. Our algorithm runs in n1−2/p · polylog(n) bits of space, which is tight up to polylogarithmic factors in n. Our algorithm also provides a (1 + Ε)-approximation to the sampled item xi with high probability using an additional Ε−2n1−2/p · polylog(n) bits of space. Interestingly, we show our techniques can be generalized to perfect polynomial samplers on turnstile streams, which is a class of functions that is not scale-invariant, in contrast to the existing perfect Lp samplers. We also achieve perfect samplers for the logarithmic function G(z) = log(1 + |z|) and the cap function G(z) = min(T, |z|p). Finally, we give an application of our results to the problem of norm/moment estimation for a subset Q of coordinates of a vector, revealed only after the data stream is processed, e.g., when the set Q represents a range query, or the set n \ Q represents a collection of entities who wish for their information to be expunged from the dataset. © 2025, CC BY.

关键词： approximation algorithms

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