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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Liu, Shanlin Zhang, Yingwei Zhao, Xudong Wang, Hesheng Northeastern University College of Information Science and Engineering Shenyang China Northeastern University State Laboratory of Synthesis Automation of Process Industry Shenyang China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350363173

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent. © 2024 IEEE.

关键词： approximation algorithms

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Differentially Private Federated Temporal Difference Learning

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2022年第11期33卷 2714-2726页

作者： Zeng, Yiming Lin, Yixuan Yang, Yuanyuan Liu, Ji SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA SUNY Stony Brook Dept Appl Math & Stat Stony Brook NY 11794 USA

This article considers a federated temporal difference (TD) learning algorithm and provides both asymptotic and finite-time analyses. To protect each worker agent's cost information from being acquired by possible attackers, we propose a privacy-preserving variant of the algorithm by adding perturbation to the exchanged information. We show the rigorous differential privacy guarantee by using moments accountant and derive an upper bound of the utility loss for the privacy-preserving algorithm. Evaluations are also provided to corroborate the efficiency of the algorithms.

关键词： Privacy Costs approximation algorithms Markov processes Differential privacy Gaussian noise Games Multi-agent reinforcement learning TD learning federated learning differential privacy

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Adaptive Hammerstein Filtering via Recursive Non-Convex Projection

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 2869-2882页

作者： Liu, Zhaoting Li, Chunguang Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Informat Proc Commun & Netw Hangzhou 310027 Peoples R China

Hammerstein model with a static nonlinearity followed by a linear filter is commonly used in numerous applications. This paper focuses on adaptive filtering techniques for parameter identification of Hammerstein systems and output prediction of nonlinear systems. By formulating the underlying filtering problem as a recursive bilinear least-squares optimization with the non-convex feasible region constraint, we develop a recursive non-convex projected least-squares (RncPLS) algorithm based on alternating direction method of multipliers (ADMM). The RncPLS algorithm alternates between implementing ridge regression and projecting on the non-convex feasible set, which successively refines the system parameters. The convergence and accuracy properties of the proposed RncPLS algorithm are theoretically investigated. Moreover, extensive simulation results in the context of system identification, nonlinear predication, and acoustic echo cancellation, are also included to demonstrate the performance characteristics of the proposed algorithm.

关键词： Signal processing algorithms Prediction algorithms Optimization Convex functions Adaptive systems Adaptation models approximation algorithms System identification nonlinear prediction adaptive filtering parameter estimation Hammerstein

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Minimum Hitting Set of Interval Bundles Problem: Computational Complexity and Approximability

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ALGORITHMICA 2022年第8期84卷 2222-2239页

作者： Gottschau, Marinus Leichter, Marilena Tech Univ Munich Dept Math Operat Res Munich Germany

The minimum hitting set of bundles problem (Mhsb) is a natural generalization of the minimum hitting set problem, where instead of hitting single elements, bundles of elements are hit. More specifically, we are given a ground set of elements and a family of sets. Every set in this family contains bundles of elements, which are subsets of the ground set. The task is to find a collection of elements of minimum size such that at least one bundle of every set in the family is hit. Motivated by several applications, we consider Mhsb restricted to interval and 2-dimensional interval bundles. We study the computational complexity and give polynomial-time algorithms for several classes of instances with these special structured bundles.

关键词： Hitting set approximation algorithms Maintenance scheduling

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Maximizing products of linear forms, and the permanent of positive semidefinite matrices

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MATHEMATICAL PROGRAMMING 2022年第1期193卷 499-510页

作者： Yuan, Chenyang Parrilo, Pablo A. MIT 77 Massachusetts Ave Cambridge MA 02139 USA

We study the convex relaxation of a polynomial optimization problem, maximizing a product of linear forms over the complex sphere. We show that this convex program is also a relaxation of the permanent of Hermitian positive semidefinite (HPSD) matrices. By analyzing a constructive randomized rounding algorithm, we obtain an improved multiplicative approximation factor to the permanent of HPSD matrices, as well as computationally efficient certificates for this approximation. We also propose an analog of van der Waerden's conjecture for HPSD matrices, where the polynomial optimization problem is interpreted as a relaxation of the permanent.

关键词： approximation algorithms

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Stream Processing of Shortest Path Query in Dynamic Road Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第5期34卷 2458-2471页

作者： Zhang, Mengxuan Li, Lei Hua, Wen Zhou, Xiaofang Univ Queensland St Lucia Qld 4072 Australia

Shortest path query in road network is pervasive in various location-based services nowadays. As the business expands, the scalability issue becomes severer and more servers are deployed to cope with it. Moreover, as the traffic condition keeps changing over time, the existing index-based approaches can hardly adapt to the real-life dynamic environment. Therefore, batch shortest path algorithms have been proposed recently to answer a set of queries together using shareable computation. Besides, they can also work in a highly dynamic environment as no index is needed. However, the existing batch algorithms either assume the batch queries are finely decomposed or just process them without differentiation, resulting in poor query efficiency. In this work, we assume the traffic condition is stable over a short period and treat the issued queries within that period as a stream of query sets. Specifically, we first propose three query set decomposition methods to cluster one query set into multiple query subsets: Zigzag that considers the 1-N shared computation;Co-Clustering that considers the source and target's spatial locality;and Search-Space-Aware that further incorporates search space estimation. After that, we propose two batch algorithms that take advantage of the previously decomposed query sets for efficient query answering: R2R that finds a set of approximate shortest paths from one region to another with bounded error;and Local Cache that improves the existing Global Cache with higher cache hit ratio. Finally, we design three efficient stream processing methods for intra-batch shared computation. The experiments on a large real-world query sets verify the effectiveness and efficiency of our decomposition methods compared with the state-of-the-art batch algorithms.

关键词： Heuristic algorithms Roads Indexes Clustering algorithms Scalability approximation algorithms Maintenance engineering Shortest path query qurey decomposition stream processing

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Scheduling Coflows With Dependency Graph

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IEEE-ACM TRANSACTIONS ON NETWORKING 2022年第1期30卷 450-463页

作者： Shafiee, Mehrnoosh Ghaderi, Javad Columbia Univ Dept Elect Engn New York NY 10027 USA

Applications in data-parallel computing typically consist of multiple stages. In each stage, a set of intermediate parallel data flows (Coflow) is produced and transferred between servers to enable starting of next stage. While there has been much research on scheduling isolated coflows, the dependency between coflows in multi-stage jobs has been largely ignored. In this paper, we consider scheduling coflows of multi-stage jobs represented by general DAGs (Directed Acyclic Graphs) in a shared data center network, so as to minimize the total weighted completion time of jobs. This problem is significantly more challenging than the traditional coflow scheduling, as scheduling even a single multi-stage job to minimize its completion time is shown to be NP-hard. In this paper, we propose a polynomial-time algorithm with approximation ratio of O(mu log(m)/log(log(m))), where mu is the maximum number of coflows in a job and m is the number of servers. For the special case that the jobs' underlying dependency graphs are rooted trees, we modify the algorithm and improve its approximation ratio. To verify the performance of our algorithms, we present simulation results using real traffic traces that show up to 53 % improvement over the prior approach. We conclude the paper by providing a result concerning an optimality gap for scheduling coflows with general DAGs.

关键词： Servers approximation algorithms Schedules Data centers Switches Job shop scheduling Task analysis Multi-stage job coflow scheduling algorithms approximation algorithms data centers

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Local Stackelberg Equilibrium Seeking in Generalized Aggregative Games

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第2期67卷 965-970页

作者： Fabiani, Filippo Tajeddini, Mohammad Amin Kebriaei, Hamed Grammatico, Sergio Univ Oxford Dept Engn Sci Oxford OX1 3PJ England Univ Tehran Sch Elect & Comp Engn Coll Engn Tehran 14395515 Iran Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands

We propose a two-layer, semidecentralized algorithm to compute a local solution to the Stackelberg equilibrium problem in aggregative games with coupling constraints. Specifically, we focus on a single-leader, multiple-follower problem, and after equivalently recasting the Stackelberg game as a mathematical program with complementarity constraints (MPCC), we iteratively convexify a regularized version of the MPCC as the inner problem, whose solution generates a sequence of feasible descent directions for the original MPCC. Thus, by pursuing a descent direction at every outer iteration, we establish convergence to a local Stackelberg equilibrium. Finally, the proposed algorithm is tested on a numerical case study, a hierarchical instance of the charging coordination problem of plug-in electric vehicles.

关键词： Games Cost function Couplings Standards approximation algorithms Convergence Wireless networks Game theory hierarchical systems optimization Stackelberg equilibrium

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New complexity and approximability results for minimizing the total weighted completion time on a single machine subject to non-renewable resource constraints

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DISCRETE APPLIED MATHEMATICS 2022年 311卷 97-109页

作者： Gyorgyi, Peter Kis, Tamas Eotvos Lorand Res Network Inst Comp Sci & Control 1111 Kende U 13-17 Budapest Hungary

We consider single machine scheduling problems with additional non-renewable resource constraints. Examples for non-renewable resources include raw materials, energy, or money. Usually they have an initial stock and replenishments arrive over time at a-priori known time points and quantities. The jobs have some requirements from the resources and a job can only be started if the available quantity from each of the required resources exceeds the requirements of the job. Upon starting a job, it consumes its requirements which decreases the available quantities of the respective non-renewable resources. There is a broad background for this class of problems. Most of the literature concentrate on the makespan, and the maximum lateness objectives. This paper focuses on the total weighted completion time objective for which the list of the approximation algorithms is very short. We extend that list by considering new special cases and obtain new complexity results and approximation algorithms. (c) 2022 Published by Elsevier B.V.

关键词： Single machine scheduling Non-renewable resources approximation algorithms FPTAS High-multiplicity scheduling

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Online Privacy-Preserving Data-Driven Network Anomaly Detection

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2022年第3期40卷 982-998页

作者： Kurt, Mehmet Necip Yilmaz, Yasin Wang, Xiaodong Mosterman, Pieter J. Columbia Univ Dept Elect Engn New York NY 10027 USA Univ S Florida Dept Elect Engn Tampa FL 33620 USA MathWorks Natick MA 01760 USA

We study online privacy-preserving anomaly detection in a setting in which the data are distributed over a network and locally sensitive to each node, and a probabilistic data model is unknown. We design and analyze a data-driven solution scheme where each node observes a high-dimensional data stream for which it computes a local outlierness score. This score is then perturbed, encrypted, and sent to a network operator. The network operator then decrypts an aggregate statistic over the network and performs online network anomaly detection via the proposed generalized cumulative sum (CUSUM) algorithm. We derive an asymptotic lower bound and an asymptotic approximation for the average false alarm period of the proposed algorithm. Additionally, we derive an asymptotic upper bound and asymptotic approximation for the average detection delay of the proposed algorithm under a certain anomaly. We show the analytical tradeoff between the anomaly detection performance and the differential privacy level, controlled via the local perturbation noise. Experiments illustrate that the proposed algorithm offers a good tradeoff between privacy and quick anomaly detection against the UDP flooding and spam attacks in a real Internet of Things (IoT) network.

关键词： Peer-to-peer computing Data models Anomaly detection Distributed databases Privacy approximation algorithms Computational modeling Network anomaly detection online data-driven distributed differential privacy privacy-anomaly detection tradeoff

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