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9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

作者： Maros, Marie Scutari, Gesualdo Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA

ISBN: (纸本)9798350344523

We investigate sparse linear regression in high-dimension via the projected LASSO estimator from distributed datasets across a network of agents. This model enables the ambient dimension to scale exponentially with the total sample size. We develop a unified algorithmic framework that encompasses a variety of distributed algorithms, new and old, such as primal, primal-dual, and gradient tracking-based methods, for which we establish both statistical and computational guarantees. Our findings demonstrate that, under standard data generation models, appropriate network connectivity and algorithm tuning, the studied schemes converge to statistically optimal estimates at a linear rate. However, the dependencies of convergence rate on the ambient dimension exhibit a noteworthy difference, ranging from linear to independent scaling. This fact is a new, interesting phenomenon distinctive of the high-dimensional setting and revealed by this study.

关键词： decentralized algorithms linear convergence LASSO estimator mesh networks statistical error

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FrogCOL and FrogMIS: new decentralized algorithms for finding large independent sets in graphs

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SWARM INTELLIGENCE 2015年第2-3期9卷 205-227页

作者： Blum, Christian Calvo, Borja Blesa, Maria J. Univ Basque Country UPV EHU San Sebastian Spain Basque Fdn Sci Ikerbasque Bilbao Spain Univ Politecn Cataluna ALBCOM Res Grp Barcelona Spain

Finding large (and generally maximal) independent sets of vertices in a given graph is a fundamental problem in distributed computing. Applications include, for example, facility location and backbone formation in wireless ad hoc networks. In this paper, we study a decentralized (or distributed) algorithm inspired by the calling behavior of male Japanese tree frogs, originally introduced for the graph-coloring problem, for its potential usefulness in the context of finding large independent sets. Moreover, we adapt this algorithm to directly produce maximal independent sets without the necessity of first producing a graph-coloring solution. Both algorithms are compared to a wide range of decentralized algorithms from the literature on a diverse set of benchmark instances for the maximal independent set problem. The results show that both algorithms compare very favorably to their competitors.

关键词： Swarm intelligence Self-desynchronization Maximal independent set decentralized algorithms

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A decentralized Framework for Kernel PCA With Projection Consensus Constraints

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第5期47卷 3908-3921页

作者： He, Fan Yang, Ruikai Shi, Lei Huang, Xiaolin Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit MOE Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Fudan Univ Sch Math Sci Shanghai Key Lab Contemporary Appl Math Shanghai 200240 Peoples R China

This paper studies kernel PCA in a decentralized setting, where data are distributively observed with full features in local nodes, and a fusion center is prohibited. Compared with linear PCA, the use of kernel brings challenges to the design of decentralized consensus optimization: the local projection directions are data-dependent. As a result, the consensus constraint in distributed linear PCA is no longer valid. To overcome this problem, we propose a projection consensus constraint and obtain an effective decentralized consensus framework, where local solutions are expected to be the projection of the global solution on the column space of the local dataset. We also derive a fully non-parametric, fast, and convergent algorithm based on the alternative direction method of multiplier, of which each iteration is analytic and communication-efficient. Experiments on a truly parallel architecture are conducted on real-world data, showing that the proposed decentralized algorithm is effective in utilizing information from other nodes and takes great advantages in running time over the central kernel PCA.

关键词： Principal component analysis Kernel Covariance matrices Vectors Optimization Support vector machines Parallel architectures Eigenvalues and eigenfunctions Costs Toy manufacturing industry Kernel methods principal component analysis consensus optimization distributed dataset decentralized algorithms

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decentralized Generalized Approximate Message-Passing for Tree-Structured Networks

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第10期70卷 7385-7409页

作者： Takeuchi, Keigo Toyohashi Univ Technol Dept Elect & Elect Informat Engn Toyohashi 4418580 Japan

decentralized generalized approximate message-passing (GAMP) is proposed for compressed sensing from distributed generalized linear measurements in a tree-structured network. Consensus propagation is used to realize average consensus required in GAMP via local communications between adjacent nodes. decentralized GAMP is applicable to all tree-structured networks that do not necessarily have central nodes connected to all other nodes. State evolution is used to analyze the asymptotic dynamics of decentralized GAMP for zero-mean independent and identically distributed Gaussian sensing matrices. The state evolution recursion for decentralized GAMP is proved to have the same fixed points as that for centralized GAMP when homogeneous measurements with an identical dimension in all nodes are considered. Furthermore, existing long-memory proof strategy is used to prove that the state evolution recursion for decentralized GAMP with the Bayes-optimal denoisers converges to a fixed point. These results imply that the state evolution recursion for decentralized GAMP with the Bayes-optimal denoisers converges to the Bayes-optimal fixed point for the homogeneous measurements when the fixed point is unique. Numerical results for decentralized GAMP are presented in the cases of linear measurements and clipping. As examples of tree-structured networks, a one-dimensional chain and a tree with no central nodes are considered.

关键词： Bayes methods Distributed algorithms Ad hoc networks Convergence Sensors Symmetric matrices Signal processing algorithms Compressed sensing generalized approximate message-passing decentralized algorithms consensus propagation tree-structured networks state evolution

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decentralized and Privacy-Preserving Learning of Approximate Stackelberg Solutions in Energy Trading Games With Demand Response Aggregators

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第10期54卷 6304-6319页

作者： Kampezidou, Styliani I. Romberg, Justin Vamvoudakis, Kyriakos G. Mavris, Dimitri N. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA

In the pathway to 2030 electricity generation decarbonization and 2050 net-zero economies, scalable integration of distributed load can support environmental goals and also help alleviate smart grid operational issues through its electricity market participation. In this work, a novel Stackelberg game theoretic framework is proposed for trading the energy bidirectionally between the demand-response (DR) aggregator and the prosumers (distributed load). This formulation allows for flexible energy arbitrage and additional monetary rewards while ensuring that the prosumers' desired daily energy demand is met. Then, a scalable (linear with the number of prosumers and the number of learning samples), the decentralized privacy-preserving algorithm is proposed to find approximate equilibria with online sampling and learning of the prosumers' cumulative best response, which finds applications beyond this energy game. Moreover, cost bounds are provided on the quality of the approximate equilibrium solution. Finally, the real data from the California day-ahead market and the UC Davis campus building energy demands are utilized to demonstrate the efficacy of the proposed framework and the algorithm.

关键词： Games Approximation algorithms Optimization Reactive power Privacy Electricity Heuristic algorithms Approximate equilibria bidirectional energy trading decentralized algorithms demand-response (DR) aggregator learning-assisted bilevel optimization privacy preservation scalable online learning Stackelberg games

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An Enhanced SD-GS-AL Algorithm for Coordinating the Optimal Power and Traffic Flows With EVs

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IEEE TRANSACTIONS ON SMART GRID 2024年第4期15卷 3904-3918页

作者： Sharma, Santosh Li, Qifeng Wei, Wei Univ Cent Florida Dept Elect Engn Orlando FL 32826 USA Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China

The electric power distribution network (PDN) and the transportation network (TN) are generally operated/coordinated by different entities. However, they are coupled through electric vehicle charging stations (EVCSs). This paper proposes to coordinate the operation of the two systems via a fully decentralized framework where the PDN and TN operators solve their own operation problems independently, with only limited information exchange. Nevertheless, the operation problems of both systems are generally mixed-integer programs (MIP), for which mature algorithms like the alternating direction method of multipliers (ADMM) may not guarantee convergence. This paper applies a novel distributed optimization algorithm called the SD-GS-AL method, which is a combination of the simplicial decomposition, gauss-seidel, and augmented Lagrangian, which can guarantee convergence and optimality for MIPs. However, the original SD-GS-AL may be computationally inefficient for solving a complex engineering problem like the PDN-TN coordinated optimization investigated in this paper. To improve the computational efficiency, an enhanced SD-GS-AL method is proposed by redesigning the inner loop of the algorithm, which can automatically and intelligently determine the iteration number of the inner loop. Simulations on the test cases show the efficiency and efficacy of the proposed framework and algorithm.

关键词： decentralized algorithms mixed-integer programs optimal power-traffic flow

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A Graph-Assisted Framework for Multiple Graph Learning

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2024年 10卷 162-178页

作者： Zhang, Xiang Wang, Qiao Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China

In this paper, we endeavor to jointly learn multiple distinct but related graphs by exploiting the underlying topological relationships between them. The difficulty lies in how to design a regularizer that accurately describes the intricate topological relationships, especially without prior knowledge. This problem becomes more challenging for the scenarios where data for different graphs are stored separately and prohibited from being transmitted to an unreliable central server due to privacy concerns. To address these issues, we propose a novel regularizer termed pattern graph to flexibly describe our priors on topological patterns. Theoretically, we provide the estimation error upper bound of the proposed graph estimator, which characterizes the impact of some factors on estimation errors. Furthermore, an approach that can automatically discover relationships among graphs is proposed to handle awkward situations without priors. On the algorithmic aspect, we develop a decentralized algorithm that updates each graph locally without sending the private data to a central server. Finally, extensive experiments on both synthetic and real data are carried out to validate the proposed method, and the results demonstrate that our framework outperforms the state-of-the-art methods.

关键词： Task analysis Laplace equations Covariance matrices Servers Data privacy Topology Signal processing algorithms Graph learning decentralized algorithms graph Laplacian topological patterns multiple graph learning

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SPARQ-SGD: Event-Triggered and Compressed Communication in decentralized Optimization

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第2期68卷 721-736页

作者： Singh, Navjot Data, Deepesh George, Jemin Diggavi, Suhas Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA 90095 USA US Army Res Lab Adelphi MD 20783 USA

In this article, we propose and analyze SParsified Action Regulated Quantized-Stochastic Gradient Descent (SPARQ-SGD), a communication-efficient algorithm for decentralized training of large-scale machine learning models over a graph with $n$ nodes, where communication efficiency is achieved using compressed exchange of local model parameters among neighboring nodes, which is triggered only when an event (a locally computable condition) is satisfied. Specifically, in SPARQ-SGD, each node takes a fixed number of local gradient steps and then checks if the model parameters have significantly changed compared to its last update;only when the change is beyond a certain threshold (specified by a design criterion), it compresses its local model parameters using both quantization and sparsification and communicates them to its neighbors. We prove that SPARQ-SGD converges as O(1/nT) and O(1/root nT) in the strongly convex and nonconvex settings, respectively, matching the convergence rates of plain decentralized SGD. This demonstrates that we get communication efficiency achieved by aggressive compression, local iterations, and event-triggered communication essentially for free. We evaluate SPARQ-SGD over real datasets to demonstrate significant amount of savings in communication over the state-of-the-art while achieving similar performance.

关键词： Consensus convergence decentralized algorithms efficient-communication event-triggered mul-tiagent systems stochastic optimization

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decentralized GENERALIZED APPROXIMATE MESSAGE-PASSING FOR TREE-STRUCTURED NETWORKS 49

DECENTRALIZED GENERALIZED APPROXIMATE MESSAGE-PASSING FOR TR...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Takeuchi, Keigo Toyohashi Univ Technol Dept Elect & Elect Inf Engn Toyohashi Aichi Japan

ISBN: (纸本)9798350344868;9798350344851

This paper proposes decentralized generalized approximate message-passing (D-GAMP) for compressed sensing in a tree-structured network. In contrast to conventional GAMP that needs a central node to gather all measurements on the network, D-GAMP utilizes consensus propagation via message-passing between adjacent nodes to achieve the same performance as centralized GAMP. The Onsager correction in D-GAMP is designed via rigorous state evolution so that the asymptotic Gaussianity of estimation errors in D-GAMP is guaranteed in each iteration for consensus propagation. By following a recently developed long-memory proof strategy, state evolution recursion for Bayes-optimal D-GAMP is proved to converge toward the Bayes-optimal fixed point-achieved by Bayes-optimal centralized GAMP-when the fixed point is unique.

关键词： Compressed sensing generalized approximate message-passing decentralized algorithms tree-structured networks state evolution

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decentralized LOW RANK MATRIX RECOVERY FROM COLUMN-WISE PROJECTIONS BY ALTERNATING GD AND MINIMIZATION 49

DECENTRALIZED LOW RANK MATRIX RECOVERY FROM COLUMN-WISE PROJ...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Moothedath, Shana Vaswani, Namrata Iowa State Univ Ames IA 50011 USA

ISBN: (纸本)9798350344868;9798350344851

This work studies our recently developed algorithm, decentralized alternating projected gradient descent algorithm (Dec-AltGDmin), for recovering a low rank (LR) matrix from independent column-wise linear projections in a decentralized setting. This means that the observed data is spread across L agents and there is no central coordinating node. Since this problem is non-convex and since it involves a subspace recovery step, most existing literature from decentralized optimization is not useful. We demonstrate using extensive numerical simulations and communication, time, and sample complexity comparisons that (i) existing decentralized gradient descent (GD) approaches fail, and (ii) other common solution approaches on LR recovery literature - projected GD, alternating GD and alternating minimization (AltMin) - either have a higher communication (and time) complexity or a higher sample complexity. Communication complexity is often the most important concern in decentralized learning.

关键词： Low Rank matrix recovery compressed sensing decentralized algorithms

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