Leader selection problem for stochastically forced consensus networks based on matrix differentiation

Leitao Gao, Guangshe Zhao, Guoqi Li and Zhaoxu Yang

Physica A: Statistical Mechanics and its Applications, 2017, vol. 469, issue C, 799-812

Abstract: The leader selection problem refers to determining a predefined number of agents as leaders in order to minimize the mean-square deviation from consensus in stochastically forced networks. The original leader selection problem is formulated as a non-convex optimization problem where matrix variables are involved. By relaxing the constraints, a convex optimization model can be obtained. By introducing a chain rule of matrix differentiation, we can obtain the gradient of the cost function which consists matrix variables. We develop a “revisited projected gradient method” (RPGM) and a “probabilistic projected gradient method” (PPGM) to solve the two formulated convex and non-convex optimization problems, respectively. The convergence property of both methods is established. For convex optimization model, the global optimal solution can be achieved by RPGM, while for the original non-convex optimization model, a suboptimal solution is achieved by PPGM. Simulation results ranging from the synthetic to real-life networks are provided to show the effectiveness of RPGM and PPGM. This works will deepen the understanding of leader selection problems and enable applications in various real-life distributed control problems.

Keywords: Leader selection; Consensus; Stochastically forced networks; Revisited projected gradient method (RPGM); Probabilistic projected gradient method (PPGM) (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:469:y:2017:i:c:p:799-812

DOI: 10.1016/j.physa.2016.11.111

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