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Distributed Constrained Online Nonconvex Optimization with Compressed Communication

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arXiv 2025年

作者： Zhang, Kunpeng Xu, Lei Yi, Xinlei Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai201210 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal–dual algorithm with compressed communication to efficiently utilize communication resources. We show that the proposed algorithm establishes an O(T max{1−θ1,θ1}) network regret bound and an O(T 1−θ1/2) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T 1−θ1). These bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with (time-varying) inequality constraints. Finally, a simulation example is presented to validate the theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

Distributed control of Descriptor Networks: A Convex Procedure for Augmented Sparsity

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arXiv 2021年

作者： Sperilă, Andrei Oară, Cristian Ciubotaru, Bogdan D. Sabău, Şerban Faculty of Automatic Control and Computers "Politehnica" University of Bucharest Sector 6 060042 Romania Electrical and Computer Engineering Department Stevens Institute of Technology HobokenNJ07030 United States

For networks of systems, with possibly improper transfer function matrices, we present a design framework which enables H∞ control, while imposing sparsity constraints on the controller’s coprime factors. We propose a convex and iterative optimization procedure with guaranteed convergence to obtain distributed controllers. By exploiting the robustness-oriented nature of our proposed approach, we provide the means to obtain sparse representations of our control laws that may not be directly supported by the network’s nominal model. © 2021, CC BY.

关键词： Convex optimization

Sublinear-time reductions for big data computing

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arXiv 2021年

作者： Gao, Xiangyu Li, Jianzhong Miao, Dongjing Department of Computer Science and Technology Harbin Institute of Technology Harbin China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the rapid popularization of big data, the dichotomy between tractable and intractable problems in big data computing has been shifted. Sublinear time, rather than polynomial time, has recently been regarded as the new standard of tractability in big data computing. This change brings the demand for new methodologies in computational complexity theory in the context of big data. Based on the prior work for sublinear-time complexity classes [9], this paper focuses on sublineartime reductions specialized for problems in big data computing. First, the pseudo-sublinear-time reduction is proposed and the complexity classes P and PsT are proved to be closed under it. To establish PsT-intractability for certain problems in P, we find the first problem in P \ PsT. Using the pseudo-sublinear-time reduction, we prove that the nearest edge query is in PsT but the algebraic equation root problem is not. Then, the pseudopolylog-time reduction is introduced and the complexity class PsPL is proved to be closed under it. The PsT-completeness under it is regarded as an evidence that some problems can not be solved in polylogarithmic time after a polynomial-time preprocessing, unless PsT = PsPL. We prove that all PsT-complete problems are also P-complete, which gives a further direction for identifying PsT-complete problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Big data

Bounding the difference between model predictive control and neural networks

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arXiv 2022年

作者： Drummond, Ross Duncan, Stephen R. Turner, Matthew C. Pauli, Patricia Allgöwer, Frank Department of Engineering Science University of Oxford 17 Parks Road OxfordOX1 3PJ United Kingdom Department of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart70569 Germany

There is a growing debate on whether the future of feedback control systems will be dominated by data-driven or model-driven approaches. Each of these two approaches has their own complimentary set of advantages and disadvantages, however, only limited attempts have, so far, been developed to bridge the gap between them. To address this issue, this paper introduces a method to bound the worst-case error between feedback control policies based upon model predictive control (MPC) and neural networks (NNs). This result is leveraged into an approach to automatically synthesize MPC policies minimising the worst-case error with respect to a NN. Numerical examples highlight the application of the bounds, with the goal of the paper being to encourage a more quantitative understanding of the relationship between data-driven and model-driven control. © 2022, CC BY.

关键词： Model predictive control

Rank-Regret Minimization

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arXiv 2021年

作者： Xiao, Xingxing Li, Jianzhong Department of Computer Science and Technology Harbin Institute of Technology Harbin China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Multi-criteria decision-making often requires finding a small representative set from the database. A recently proposed method is the regret minimization set (RMS) query. RMS returns a size r subset S of dataset D that minimizes the regret-ratio (the difference between the score of top-1 in S and the score of top-1 in D, for any possible utility function). RMS is not shift invariant, causing inconsistency in results. Further, existing work showed that the regret-ratio is often a "made up" number and users may mistake its absolute value. Instead, users do understand the notion of rank. Thus it considered the problem of finding the minimal set S with a rank-regret (the rank of top-1 tuple of S in the sorted list of D) at most k, called the rank-regret representative (RRR) problem. Corresponding to RMS, we focus on the min-error version of RRR, called the rank-regret minimization (RRM) problem, which finds a size r set to minimize the maximum rank-regret for all utility functions. Further, we generalize RRM and propose the restricted RRM (i.e., RRRM) problem to optimize the rank-regret for functions restricted in a given space. Previous studies on both RMS and RRR did not consider the restricted function space. The solution for RRRM usually has a lower regret level and can better serve the specific preferences of some users. Note that RRM and RRRM are shift invariant. In 2D space, we design a dynamic programming algorithm 2DRRM to return the optimal solution for RRM. In HD space, we propose an algorithm HDRRM that introduces a double approximation guarantee on rank-regret. Both 2DRRM and HDRRM are applicable for RRRM. Extensive experiments on the synthetic and real datasets verify the efficiency and effectiveness of our algorithms. In particular, HDRRM always has the best output quality in experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision making

Scale Fragilities in Localized Consensus Dynamics

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arXiv 2022年

作者： Tegling, Emma Bamieh, Bassam Sandberg, Henrik Department of Automatic Control Lund University P.O. Box 118 LundSE-221 00 Sweden Department of Mechanical Engineering The University of California at Santa Barbara Santa BarbaraCA93106 United States School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden

We consider distributed consensus in networks where the agents have integrator dynamics of order two or higher (n ≥ 2). We assume all feedback to be localized in the sense that each agent has a bounded number of neighbors and consider a scaling of the network through the addition of agents in a modular manner, i.e., without re-tuning controller gains upon addition. We show that standard consensus algorithms, which rely on relative state feedback, are subject to what we term scale fragilities, meaning that stability is lost as the network scales. For high-order agents (n ≥ 3), we prove that no consensus algorithm with fixed gains can achieve consensus in networks of any size. That is, while a given algorithm may allow a small network to converge, it causes instability if the network grows beyond a certain finite size. This holds in families of network graphs whose algebraic connectivity, that is, the smallest non-zero Laplacian eigenvalue, is decreasing towards zero in network size (e.g. all planar graphs). For second-order consensus (n = 2) we prove that the same scale fragility applies to directed graphs that have a complex Laplacian eigenvalue approaching the origin (e.g. directed ring graphs). The proofs for both results rely on Routh-Hurwitz criteria for complex-valued polynomials and hold true for general directed network graphs. We survey classes of graphs subject to these scale fragilities, discuss their scaling constants, and finally prove that a sub-linear scaling of nodal neighborhoods can suffice to overcome the issue. Copyright © 2022, The Authors. All rights reserved.

关键词： State feedback

Decentralized Nonconvex Robust Optimization over Unsafe Multiagent Systems: System Modeling, Utility, Resilience, and Privacy Analysis

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arXiv 2024年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Cheng, Huqiang Guo, Xiaoyu Huang, Tingwen School of Automation Central South University Changsha410083 China Department of Biomedical Engineering City University of Hong Kong Kowloon Hong Kong School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Key Laboratory of Dependable Services Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing400044 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Privacy leakage and Byzantine failures are two adverse factors to the intelligent decision-making process of multiagent systems (MASs). Considering an unsafe MAS with these two issues, this paper targets the resolution of a class of nonconvex optimization problems under the Polyak-Lojasiewicz (P-L) condition. To address this problem, we first identify and construct the unsafe MAS model. Under this kind of unfavorable MASs, we mask the local gradients with Gaussian noises and adopt a resilient aggregation method self-centered clipping (SCC) to design a differentially private (DP) decentralized Byzantine-resilient algorithm, namely DP-SCC-PL, which simultaneously achieves differential privacy and Byzantine resilience. The convergence analysis of DP-SCC-PL is challenging since the convergence error can be contributed jointly by privacy-preserving and Byzantine-resilient mechanisms, as well as the nonconvex relaxation, which is addressed via seeking the contraction relationships among the disagreement measure of reliable agents before and after SCC aggregation, together with the optimal gap. Theoretical results reveal that DP-SCC-PL achieves consensus among all reliable agents and sublinear (inexact) convergence with well-designed step-sizes. It has also been proved that if there are no privacy issues and Byzantine agents, then the asymptotic exact convergence can be recovered. Numerical experiments verify the utility, resilience, and differential privacy of DP-SCC-PL by tackling a nonconvex optimization problem satisfying the P-L condition under various Byzantine attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

MEST: Multi-plane Embedding and Spatial-temporal Transformer for Parkinson’s disease diagnosis

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MEST: Multi-plane Embedding and Spatial-temporal Transformer...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jin Liu Hao Du Qian Bi Haiyan Liao Yi Pan School of Computer Science and Engineering Central South University Changsha China School of Mathematics and statistics Hunan Frist Normal University Changsha China Department of Radiology the Second Xiangya Hospital Central South University Changsha China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Parkinson’s disease (PD) is a common neurodegenerative disorder that impairs the patient’s quality of life. Medical imaging technology has provided a variety of neuroimages for PD diagnosis studies. However, how to effectively integrate the rich representations from multi-modality data is still a challenging task. To address this challenging task, we propose a multiplane embedding and spatial-temporal Transformer (MEST) framework for PD diagnosis. Firstly, we project structural magnetic resonance imaging (sMRI) into 2D images from coronal, sagittal and axial directions, respectively. Then, the multi-plane dynamic images are learned by pre-trained VGG11 and attention mechanism for representation learning. Afterwards, time series information of functional magnetic resonance imaging (fMRI) is used to construct dynamic functional connection images. To capture information changes in brain, spatial-temporal connectivity Transformer (SCTransformer) is utilized to address spatial-temporal redundancy and dependencies. To integrate multimodality data, ensemble learning is designed based on majority voting strategy to perform PD diagnosis. We evaluate the proposed method on 279 subjects from an in-house and Parkinsons Progression Markers Initiative (PPMI) dataset. Experimental results show that the MEST achieves promising performance with accuracies of 0.856 and 0.885, and outperforms some state-of-the-art methods.

关键词： Representation learning Time series analysis Redundancy Functional magnetic resonance imaging Transformers Medical diagnosis Ensemble learning

Fault-tolerant control of semi-active suspension in case of oil leakage of magnetorheological damper

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Fault-tolerant control of semi-active suspension in case of ...

European control Conference (ECC)

作者： Hakan Basargan Filip Jeniš András Mihály Péter Gáspár Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Stoczek u. 2 Budapest Hungary Faculty of Mechanical Engineering Institute of Machine and Industrial Designs Brno University of Technology Technicka 2 Brnos Czech Republic Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Kende u. 13-17 Budapest Hungary

The paper presents a reconfigurable fault-tolerant control strategy for a semi-active suspension using magnetorheological (MR) damper. The aim of the control reconfiguration is to handle the adverse behaviour of the MR damper due to oil leakage induced by the wear of the suspension component. The proposed method relies on the data driven model of the MR damper, using an estimation procedure to quantify the healthiness of the damper and to estimate the performance degradation due to the oil leakage. The reconfiguration control strategy is founded on the Linear Parameter Varying (LPV) framework, where a scheduling variable is defined to represent the healthiness level of the MR damper. By the scaling of the control action through the scheduling variable, the performance degradation of the MR damper can be compensated to match the behaviour of the healthy dampers. The proposed method is demonstrated through simulations, comparing the performance of the fault-tolerant LPV control to conventional semi-active control methods

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