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In this brief, the problem of distributively solving a mixed equilibrium problem (EP) with multiple sets is investigated. A network of agents is employed to cooperatively find a point in the intersection of multiple convex sets ensuring that the sum of multiple bifunctions with a free variable is nonnegative. Each agent can only access information associated with its own bifunction and a local convex set. To solve this problem, a distributed algorithm involving a fixed step size is proposed by combining the mirror descent algorithm, the primal-dual algorithm, and the consensus algorithm. Under mild conditions on bifunctions and the graph, we prove that all agents’ states asymptotically converge to a solution of the mixed EP. A numerical simulation example is provided for demonstrating the effectiveness of theoretical results. [ABSTRACT FROM AUTHOR]

This article aims to address distributed optimization problems over directed and time-varying networks, where the global objective function consists of a sum of locally accessible convex objective functions subject to a feasible set constraint and coupled inequality constraints whose information is only partially accessible to each agent. For this problem, a distributed proximal-based algorithm, called distributed proximal primal-dual algorithm, is proposed based on the celebrated centralized proximal point algorithm. It is shown that the proposed algorithm can lead to the global optimal solution with a general step size, which is diminishing and nonsummable, but not necessarily square summable, and the saddle-point running evaluation error vanishes proportionally to O(1√k) , where k > 0 is the iteration number. Finally, a simulation example is presented to corroborate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this technical note, a distributed algorithm is proposed for multiagent networks to achieve a least squares solution of a system of linear equations, in which each agent only knows part of the overall equations and communicates only with its nearby neighbors. The proposed algorithm is discrete time but does not involve small or time-varying step sizes. Given that the network is fixed, connected, and undirected, the proposed algorithm enables all agents in the network to achieve exponentially fast the same least squares solution; this is validated by simulations. [ABSTRACT FROM AUTHOR]

This paper proposes distributed discrete-time algorithms to cooperatively solve an additive cost optimization problem in multiagent networks. The striking feature lies in the use of only the sign of relative state information between neighbors, which substantially differentiates our algorithms from others in the existing literature. We first interpret the proposed algorithms in terms of the penalty method in optimization theory and then perform nonasymptotic analysis to study convergence for static network graphs. Compared with the celebrated distributed subgradient algorithms, which, however, use the exact relative state information, the convergence speed is essentially not affected by the loss of information. We also study how introducing noise into the relative state information and randomly activated graphs affect the performance of our algorithms. Finally, we validate the theoretical results on a class of distributed quantile regression problems. [ABSTRACT FROM AUTHOR]

This paper concentrates on the integration of relative localization and the formation control over leader–follower networks. First, we develop a consensus-like relative localization scheme for each agent to estimate the real-time relative positions of its neighbors by merely using velocity as well as distance-related measurements and local communications under the assumption that the local frames of all agents share a common orientation. It is shown that the relative position estimate exponentially converges to its true value under a persistent excitation condition on relative velocity between the two agents. Second, an integrated relative localization and a leader–follower formation control are developed by combining the proposed relative localization scheme and a complex Laplacian-based formation control scheme. It is proven that the integrated system globally asymptotically converges to a stationary similar formation in the case of error-free initial relative position estimates while globally uniformly bounds in a neighborhood of the prescribed formation in the case with initial relative position estimation errors. Furthermore, based on the two degrees of freedom of similar formation on translation, the integrated scheme is also generalized to achieve moving similar formation control. Finally, simulations are presented to illustrate the effectiveness of our theoretical results. [ABSTRACT FROM AUTHOR]

This paper develops a fast distributed algorithm, termed DEXTRA, to solve the optimization problem when n agents reach agreement and collaboratively minimize the sum of their local objective functions over the network, where the communication between the agents is described by a directed graph. Existing algorithms solve the problem restricted to directed graphs with convergence rates of O(\ln k/\sqrt{k}) for general convex objective functions and O(\ln k/k) when the objective functions are strongly convex, where  k is the number of iterations. We show that, with the appropriate step-size, DEXTRA converges at a linear rate O(\tau ^{k}) for 0<\tau <1$ , given that the objective functions are restricted strongly convex. The implementation of DEXTRA requires each agent to know its local out-degree. Simulation examples further illustrate our findings. [ABSTRACT FROM AUTHOR]

This paper discusses consensus problems for multiagent networks under directed communication graphs. The motions of agents are described by impulsive differential equations, and thus, consensus algorithms can be designed in terms of impulsive systems. Different from the standard consensus algorithms which rely on continuous-time or discrete-time models, the proposed algorithms based on impulsive systems take advantages of instantaneous information. It is shown that the proposed algorithms have a faster convergence speed than the standard consensus algorithms. Moreover, conditions under which all agents reach consensus with the desired performance are presented for the multiagent networks with external disturbances. Simulation results demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]