12 results on '"Wang, Le Yi"'
Search Results
2. Stochastic Adaptive Optimization With Dithers.
- Author
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Xie, Siyu, Liang, Shu, Wang, Le Yi, Yin, George, and Chen, Wen
- Subjects
MATHEMATICAL optimization ,PARAMETER identification ,STOCHASTIC processes ,PARAMETER estimation ,NOISE measurement - Abstract
Optimization methods are essential and have been used extensively in a broad spectrum of applications. Most existing literature on optimization algorithms does not consider systems that involve unknown system parameters. This article studies a class of stochastic adaptive optimization problems in which identification of unknown parameters and search for the optimal solutions must be performed simultaneously. Due to a fundamental conflict between parameter identifiability and optimality in such problems, we introduce a method of adding stochastic dither signals into the system, which provide sufficient excitation for estimating the unknown parameters, leading to convergent adaptive optimization algorithms. Joint identification and optimization algorithms are developed and their simultaneous convergence properties of parameter estimation and optimization variable updates are proved. Under both noise-free and noisy observations, the corresponding convergence rates are established. The main results of this article reveal certain fundamental relationships and tradeoff among updating step sizes, dither magnitudes, parameter estimation errors, optimization accuracy, and convergence rates. Simulation case studies are used to illustrate the adaptive optimization algorithms and their main properties. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
3. Dual Averaging Push for Distributed Convex Optimization Over Time-Varying Directed Graph.
- Author
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Liang, Shu, Wang, Le Yi, and Yin, George
- Subjects
- *
ALGORITHMS , *SUBGRADIENT methods , *DISTRIBUTED algorithms , *DIRECTED graphs , *NONSMOOTH optimization , *MULTIAGENT systems , *CONVEX functions - Abstract
Inspired by the subgradient push method developed recently by Nedić et al. we present a distributed dual averaging push algorithm for constrained nonsmooth convex optimization over time-varying directed graph. Our algorithm combines the dual averaging method with the push-sum technique and achieves an $O(1/ \sqrt{k})$ convergence rate. Compared with the subgradient push algorithm, our algorithm, first, addresses the constrained problems, and, second, has a faster convergence rate, and, third, simplifies the convergence analysis. We also generalize the proposed algorithm so that input variables of subgradient oracles have guaranteed convergence. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
4. Distributed Smooth Convex Optimization With Coupled Constraints.
- Author
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Liang, Shu, Wang, Le Yi, and Yin, George
- Subjects
- *
SUBGRADIENT methods , *NONSMOOTH optimization , *LINEAR programming , *CONVEX functions , *HEURISTIC algorithms , *DISTRIBUTED algorithms , *ALGORITHMS , *MATHEMATICAL equivalence - Abstract
This note develops a distributed algorithm to solve a convex optimization problem with coupled constraints. Both coupled equality and inequality constraints are considered, where functions in the equality constraints are affine and functions in the inequality constraints are convex. Different from primal-dual subgradient methods with decreasing stepsizes for nonsmooth optimizations, our algorithm focuses on smooth problems and uses a fixed stepsize to find the exact optimal solution. Convergence analysis is derived with rigorous proofs. Our result is also illustrated by simulations. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
5. Two-Time-Scale Hybrid Traffic Models for Pedestrian Crowds.
- Author
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Wang, Qianling, Dong, Hairong, Ning, Bin, Wang, Le Yi, and Yin, George
- Abstract
This paper introduces new models to describe pedestrian crowd dynamics in a typical unidirectional environment, such as corridors, pathways, and railway platforms. Pedestrian movements are represented in a two-dimensional space that is further divided into narrow virtual lanes. Consequently, pedestrians either move in a lane following each other or change lanes, when it is desirable. Within this framework, the motions of pedestrians are modeled as a two-dimensional and two-time-scale hybrid system. A pedestrian’s movement along the crowd direction is labeled as the $x$ direction and modeled by a real-valued process, a solution of a differential equation in continuous time, the lane change is labeled as the $y$ direction. In contrast to the $x$ direction dynamics, the movements in the $y$ direction only happen at some time epoch. Although the movements are still on the same time horizon as the $x$ direction movements, with a slight abuse of notation and for simplicity and convenience, we use discrete time as the time indicator, and model the movements by a recursive equation taking values in a finite set. Under common assumptions of crowd movements, we prove that the crowd movements in the $x$ direction will converge to a uniform distance distribution and the convergence rate is exponential. Furthermore, by using a velocity-distance function to represent the common crowd and traffic congestion scenarios, we show that all pedestrians will asymptotically move with a uniform group speed. In the $y$ direction, when pedestrians naturally wish to change to faster lanes, we show that the numbers in each virtual lanes converge to a balanced distribution and hence achieves asymptotic consensus as shown typically in a crowd behavior. Stability and convergence analysis is carried out rigorously by using properties of circular matrices, stability of networked systems, and stochastic approximations. Simulation studies are used to demonstrate the main properties of our modeling approach and establish its usefulness in representing pedestrian dynamics. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
6. Optimal Power Management in DC Microgrids With Applications to Dual-Source Trolleybus Systems.
- Author
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Zhang, Di, Wang, Le Yi, Jiang, Jiuchun, and Zhang, Weige
- Abstract
This paper investigates optimal power management in dc microgrids, with its applications to dc dual-source trolleybus systems. High mobility of buses and their impact on power supply networks introduce challenging power management issues. This paper incorporates line power losses in power management strategies and introduces a new distributed optimal power management methodology. A multi-objective optimization model is developed. Using only neighborhood information exchange among feeder lines in the network, the new consensus-type control accommodates both feeder current allocation and power loss reduction with fast convergence. One critical finding of this paper is that our local recursive optimization algorithms achieve the global optimal solution asymptotically. Under random noise on information exchange, convergence and optimality of the proposed method are established rigorously. The power system configurations of the Beijing dual-source trolleybus system are used for simulation case studies on the new power management methods. Feasibility, accuracy, and comparison with global optimization results are demonstrated. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
- Full Text
- View/download PDF
7. Impact of Communication Erasure Channels on Control Performance of Connected and Automated Vehicles.
- Author
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Nguyen, Thu, Wang, Le Yi, Yin, George, Zhang, Hongwei, Li, Shengbo Eben, and Li, Keqiang
- Subjects
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AUTONOMOUS vehicles , *WIRELESS communications , *AUTOMOTIVE transportation , *INTELLIGENT transportation systems , *MOBILE communication systems , *TELECOMMUNICATION systems - Abstract
Connected and automated vehicles mandate integrated design of communications and control to achieve coordination of highway vehicles. Random features of wireless communications introduce new types of uncertainties into networked systems and impact control performance significantly. Due to typical packet loss, erasure channels create random link interruption and switching in network topologies. This paper models such switching network topologies by Markov chains and derives their probability transition matrices from stochastic characterizations of the channels. Impact of communication erasure channels on vehicle platoon formation and robustness under a weighted and constrained consensus framework is analyzed. By comparing convergence properties of networked control algorithms under different communication channel features, we characterize some intrinsic relationships between packet delivery ratio and convergence rate. Simulation case studies are performed to verify the theoretical findings. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
- Full Text
- View/download PDF
8. Switching Stochastic Approximation and Applications to Networked Systems.
- Author
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Yin, George, Wang, Le Yi, and Nguyen, Thu
- Subjects
- *
STOCHASTIC processes , *APPROXIMATION algorithms , *MARKOV processes , *SWITCHING systems (Telecommunication) , *TELECOMMUNICATION systems , *STOCHASTIC approximation , *STOCHASTIC analysis - Abstract
This paper investigates the interaction between control and communications in networked systems by studying a class of stochastic approximation algorithms that accommodate random network topology switching processes, time-varying functions, nonlinear dynamics, additive and nonadditive noises, and other uncertainties. Interaction among control strategy and the multiple stochastic processes introduces critical challenges in such problems. By modeling the random switching as a discrete-time Markov chain and studying multiple stochastic uncertainties in a unified framework, it is shown that under broad conditions, the algorithms are convergent. The performance of the algorithms is further analyzed by establishing their rate of convergence and asymptotic characterizations. Simulation case studies are conducted to evaluate the performance of the procedures in various aspects. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
9. Critical Connectivity and Fastest Convergence Rates of Distributed Consensus With Switching Topologies and Additive Noises.
- Author
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Chen, Ge, Wang, Le Yi, Chen, and Yin, George
- Subjects
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STOCHASTIC approximation , *MULTIAGENT systems , *BIOLOGISTS , *PHYSICISTS , *ANIMAL behavior - Abstract
Consensus conditions and convergence speeds are crucial for distributed consensus algorithms of networked systems. Based on a basic first-order average-consensus protocol with time-varying topologies and additive noises, this paper first investigates its critical consensus condition on network topology by stochastic approximation frameworks. A new joint-connectivity condition called extensible joint-connectivity that contains a parameter $\delta$ (termed the extensible exponent) is proposed. With this and a balanced topology condition, we show that a critical value of \delta$ for consensus is 1/2$ . Optimization on convergence rate of this protocol is further investigated. It is proved that the fastest convergence rate, which is the theoretic optimal rate among all controls, is of the order 1/t for the worst topologies, which are balanced and satisfy the extensible joint-connectivity condition. For practical implementation, certain open-loop control strategies are introduced to achieve consensus with a convergence rate of the same order as the fastest convergence rate. Furthermore, a consensus condition is derived for nonstationary and strongly correlated random topologies. The algorithms and consensus conditions are applied to distributed consensus computation of mobile ad-hoc networks; and their related critical exponents are derived from relative velocities of mobile agents for guaranteeing consensus. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
10. Recursive Identification of Hammerstein Systems: Convergence Rate and Asymptotic Normality.
- Author
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Mu, Biqiang, Chen, Han-Fu, Wang, Le Yi, Yin, George, and Zheng, Wei Xing
- Subjects
HAMMERSTEIN equations ,RECURSIVE functions ,SYSTEM identification ,ASYMPTOTIC normality ,STOCHASTIC approximation ,KERNEL functions - Abstract
In this work, recursive identification algorithms are developed for Hammerstein systems under the conditions considerably weaker than those in the existing literature. For example, orders of linear subsystems may be unknown and no specific conditions are imposed on their moving average part. The recursive algorithms for estimating both linear and nonlinear parts are based on stochastic approximation and kernel functions. Almost sure convergence and strong convergence rates are derived for all estimates. In addition, the asymptotic normality of the estimates for the nonlinear part is also established. The nonlinearity considered in the paper is more general than those discussed in the previous papers. A numerical example verifies the theoretical analysis with simulation results. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
11. Decision-Based System Identification and Adaptive Resource Allocation.
- Author
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Guo, Jin, Mu, Biqiang, Wang, Le Yi, Yin, George, and Xu, Lijian
- Subjects
ADAPTIVE control systems ,RESOURCE allocation -- Mathematical models ,IDENTIFICATION documents ,ARTIFICIAL intelligence ,FEEDBACK control systems - Abstract
System identification extracts information from a system's operational data to derive a representative model for the system so that a decision can be made with desired accuracy and reliability. When resources are limited, especially for networked systems sharing data and communication power and bandwidth, identification must consider complexity as a critical limitation. Focusing on optimal resource allocation under a given reliability requirement, this paper studies identification complexity and its relations to decision making. Dynamic resource assignments are investigated. Algorithms are developed and their convergence properties are established, including strong convergence, almost sure convergence rate, and asymptotic normality. By a suitable design of resource updating step sizes, the algorithms are shown to achieve the CR lower bound asymptotically, and hence are asymptotically efficient. Illustrative examples demonstrate significant advantages of our real-time and individualized resource allocation methodologies over population-based worst-case strategies. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
12. System Identification Under Regular, Binary, and Quantized Observations: Moderate Deviations Error Bounds.
- Author
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He, Qi, Yin, G. George, and Wang, Le Yi
- Subjects
SAMPLING errors ,SYSTEM identification ,PROBABILISTIC databases ,REAL-time computing ,BINARY number system - Abstract
This technical note presents new results on probabilistic characterization of identification errors in their relationships to data sizes and accuracy requirements. Employing the moderate deviations principle, this technical note shows that if the identification accuracy progressively increases with a suitable rate, the probability of an estimate going outside the precision bounds decays exponentially with the data size. The precise rate of the decaying probability is obtained. System identification under regular, binary, and quantized observations are considered. Impact of unmodeled dynamics is also investigated. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
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