Distributed optimisation for resource allocation with event-triggered communication over general directed topology

Shi, Xiasheng; Wang, Yuandong; Song, Sumian; Yan, Gangfeng

doi:10.1080/00207721.2018.1439124

Cited by 22 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The extension to general convex functions is performed in [16] by adopting the nonnegative surplus method, at the expense of a slower convergence rate. The ADMM-based algorithms are developed in [17], [18], and algorithms that aim to handle communication delay in time-varying networks and perform event-triggered updates are studied in [19] and [20], respectively. We note that all the above-mentioned works [1], [15]- [20] do not provide explicit convergence rate for their algorithms.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…The ADMM-based algorithms are developed in [17], [18], and algorithms that aim to handle communication delay in time-varying networks and perform event-triggered updates are studied in [19] and [20], respectively. We note that all the above-mentioned works [1], [15]- [20] do not provide explicit convergence rate for their algorithms. In contrast, the DCGT of this work is proved to achieves a linear convergence rate for strongly convex and Lipschitz smooth cost functions, and has a sublinear convergence rate without the Lipschitz smoothness.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…where the last inequality used (12). Recall (11) and x 20) where the first inequality follows from the strong convexity of F i (w), and we implicitly assume F i (w) is differentiable to simply the presentation. The same result holds for nondifferentiable functions by introducing subgradients.…”

Section: Remarkmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Dual Gradient Tracking for Resource Allocation in Unbalanced Networks

Zhang

You

Cai

2020

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

This paper proposes a distributed conjugate gradient tracking algorithm (DCGT) to solve resource allocation problems in a possibly unbalanced network, where each node of the network computes its optimal resource via interacting only with its neighboring nodes. Our key idea is the novel use of the celebrated AB algorithm to the dual of the resource allocation problem. To study the convergence of DCGT, we first establish the sublinear convergence of AB for non-convex objective functions, which advances the existing results on AB as they require the strong-convexity of objective functions. Then we show that DCGT converges linearly for strongly convex and Lipschitz smooth objective functions, and sublinearly without the Lipschitz smoothness. Finally, simulation results validate that DCGT outperforms state-of-the-art algorithms in distributed resource allocation problems.

show abstract

Section: A Literature Reviewmentioning

confidence: 99%

Section: A Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Dual Gradient Tracking for Resource Allocation in Unbalanced Networks

Zhang

You

Cai

2020

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…To solve the distributed resource allocation problem, some gradient‐based algorithms have been proposed [11], followed by game theory [12, 13]. So‐called consensus‐based algorithms have also been proposed [14–17]. For instance, [2] proposed a surplus‐based distributed algorithm for solving the resource allocation problem in which the local cost function is quadratic.…”

Section: Introductionmentioning

confidence: 99%

An exponentially convergent distributed algorithm for resource allocation problem

Shi

Zheng

Lin

et al. 2020

Asian Journal of Control

Self Cite

View full text Add to dashboard Cite

In this paper, the distributed resource allocation problem over undirected networks, considering box constraints and demand–supply constraints, is studied. Furthermore, each agent has one private cost function that is only known by itself. The aim is to minimize the sum of all local cost functions in a distributed manner while meeting the constraints. To solve the dispatch problem, a continuous‐time distributed algorithm with fixed step‐size is proposed. After that, a modified distributed algorithm of discrete‐time multi‐agent system is provided. Moreover, the proposed algorithms obtain the global optimal solution with an exponential convergence speed by the conventional Lyapunov methods. Finally, several simulations of IEEE‐14 bus are presented to validate and illustrate the proposed designed algorithm.

show abstract

“…[18] proposed one event-triggered distributed algorithm for solving the economic dispatch problem over discrete-time multi-agents under time-varying undirected graphs. For solving the economic dispatch problem, we have proposed one discrete-time distributed event-triggered algorithm over directed networks in [19]. However, there is little paper concerned about this topic in continuous-time systems [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Distributed optimization for economic power dispatch with event‐triggered communication

Shi

Zheng

Lin

et al. 2019

Asian Journal of Control

Self Cite

View full text Add to dashboard Cite

This paper focuses on the economic power dispatch problem in a smart grid by using the distributed consensus with event-triggered communication mechanism based on continuous-time multi-agent systems over balanced directed networks, where there are many generation units working cooperatively to achieve an optimal solution. The event-triggered communication mechanism means that the information transmission between agents is triggered by event instead of time. The triggering condition and algorithm for each agent are fully decentralized. At each instant time, each agent updates its state by employing the states collected from itself and its neighboring agents at their last triggering time. Finally, two examples are given to analyze the feasibility of above proposed algorithm.

show abstract

Distributed optimisation for resource allocation with event-triggered communication over general directed topology

Cited by 22 publications

References 30 publications

Distributed Dual Gradient Tracking for Resource Allocation in Unbalanced Networks

Distributed Dual Gradient Tracking for Resource Allocation in Unbalanced Networks

An exponentially convergent distributed algorithm for resource allocation problem

Distributed optimization for economic power dispatch with event‐triggered communication

Contact Info

Product

Resources

About