Distributed Optimization for Linear Multiagent Systems: Edge- and Node-Based Adaptive Designs

Zhao, Yu; Liu, Yongfang; Wen, Guanghui; Chen, Guanrong

doi:10.1109/tac.2017.2669321

Cited by 225 publications

(110 citation statements)

References 34 publications

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“…Thus, it is worth analyzing and extending it in this area further. Zhao et al [23][24][25] have introduced the motion-planning approaches to solve the distributed consensus problems and studied the distributed optimization problem for continuoustime multiagent systems with general linear dynamics and finite-time tracking problem of a multiagent system with second-order nonlinear dynamics by providing a numerical example to illustrate the effectiveness of the analytical results, obtaining some valuable conclusions.…”

Section: Complexitymentioning

confidence: 99%

Research on a 3D Predator‐Prey Evolutionary System in Real Estate Market

Yang

Tang

2018

Complexity

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This paper establishes a model on the upstream and downstream relationship among private enterprises, provincial and local officials, and the central government in the real estate market using the population ecology theory of mutual relations among individual species from the perspective of business ecosystem. A dynamic model is introduced and the complex dynamical behaviors of such a predator-prey model are investigated by means of numerical simulation. The local stability conditions and complex dynamics are investigated, and the existence of chaos is discussed in the sense of Marotto theorem; bifurcation diagrams, Lyapunov exponents, sensitivity analysis for initial values, and time history figure of the system are mapped out and discussed. This shows that there are two routes to complicated dynamics, one of which is the cascade of flip bifurcations resulting in periodic cycles (and chaos), and the other one is Neimark-Sacker bifurcation which produces attractive invariant closed curves. We arrive at conclusions that the phenomenon of chaos is harmful to private enterprises, and unstable behavior is often unfavorable. Thus, linear feedback control is applied to drive the model to a stable state when the system exhibits chaotic behaviors, achieving the goal of eliminating the negative effects to a large extent.

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Section: Complexitymentioning

confidence: 99%

Research on a 3D Predator‐Prey Evolutionary System in Real Estate Market

Yang

Tang

2018

Complexity

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“…An important class of distributed optimization problems is to minimize a global objective function which is the sum of local objective functions, by local computation and information exchange with neighboring agents. This kind of distributed optimization problems have been addressed by many researchers from various perspectives (see, e.g., [5]- [20]). …”

Section: Introductionmentioning

confidence: 99%

“…By designing the consensusbased dynamics, these discrete-time algorithms can find the solution of the optimization problem. Recently, continuoustime algorithms have been introduced to solve distributed optimization problems (see, e.g., [11]- [20]). In [14], [16] and [19], the Newton-Raphson and the Zero-Gradient-Sum based continuous-time algorithms achieve the global convergence on undirected graphs using the positive bounded Hessian of local objective functions.…”

Section: Introductionmentioning

confidence: 99%

“…The projection based algorithm in [15] removes the requirement of the twice differentiability of local objective functions by using the projection of their gradients. Two adaptive schemes are designed in [20] to solve the distributed optimization problem for general linear dynamics with undirected communications. The global convergence is established when local objective functions are convex and local gradients are error-bounded.…”

Section: Introductionmentioning

confidence: 99%

“…Two main challenges for the distributed optimization with directed communications are 1) removing the requirement of global information, and establishing global convergence with only locally Lipschitz gradients; 2) relaxing the common assumption (see, e.g., [11]- [20], [22]) of convexity of local objective functions. It is well-known in the consensus control design that adaptive techniques can be used to deal with the unknown network connectivity (see, e.g., [23]- [25]).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Adaptive Convex Optimization on Directed Graphs via Continuous-Time Algorithms

Ding

Sun

et al. 2018

IEEE Trans. Automat. Contr.

154

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Abstract-This note considers the distributed optimization problem on directed graphs with nonconvex local objective functions and the unknown network connectivity. A new adaptive algorithm is proposed to minimize a differentiable global objective function. By introducing dynamic coupling gains and updating the coupling gains using relative information of system states, the nonconvexity of local objective functions, unknown network connectivity and the uncertain dynamics caused by locally Lipschitz gradients are tackled concurrently. Consequently, the global asymptotic convergence is established when the global objective function is strongly convex and the gradients of local objective functions are only locally Lipschitz. When the communication graph is strongly connected and weight-balanced, the algorithm is independent of any global information. Then, the algorithm is naturally extended to unbalanced directed graphs by using the left eigenvector of the Laplacian matrix associated with the zero eigenvalue. Several numerical simulations are presented to verify the results.

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An average queue‐length‐difference–based congestion detection algorithm in TCP/AQM network

Zhu

Luo

Yang

et al. 2018

Adaptive Control & Signal

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Congestion detection in transmission control protocol/active queue management networks remains a challenging problem in which the choosing of congestion signal is one of the most important factors. Exponentially weighted moving average of queue length, the most widely used congestion signal, is facing difficulties in detecting incipient congestion and quantifying the optimal forgetting factor. Aiming at these 2 disadvantages, we propose an average queue-length-difference-based congestion detection algorithm where exponentially weighted moving average of queue-length difference is chosen as the congestion signal with the theoretical optimal forgetting factor deduced. First, by defining the queue-length difference as the state, the corresponding state equation is derived from the fluid model. Second, we prove that the inflow traffic in state equation is a discrete-time martingale, which can be transformed to a Wiener process according to the martingale representation theorem. Noticing that the observation of state will be coupled with noise because of the unstable transmission, the state estimation is then derived with the application of recursive least squares filter. The filter gain of state estimation, which is a function of the noise-signal ratio, corresponds to the optimal forgetting factor in average queue-length-difference-based congestion detection algorithm. Simulation results in NS-3 and MATLAB illustrate the effectiveness of the proposed algorithm.

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Distributed Optimization for Linear Multiagent Systems: Edge- and Node-Based Adaptive Designs

Cited by 225 publications

References 34 publications

Research on a 3D Predator‐Prey Evolutionary System in Real Estate Market

Research on a 3D Predator‐Prey Evolutionary System in Real Estate Market

Distributed Adaptive Convex Optimization on Directed Graphs via Continuous-Time Algorithms

An average queue‐length‐difference–based congestion detection algorithm in TCP/AQM network

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