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This paper studies the problem of optimizing the sum of multiple agents' local convex objective functions, subject to global convex inequality constraints and a convex state constraint set over a network. Through characterizing the primal and dual optimal solutions as the saddle points of the Lagrangian function associated with the problem, we propose a(More)
This paper deals with the distributed discrete-time coordinated tracking problem for multi-agent systems with Markovian switching topologies. In the multi-agent team, only some of the agents can obtain the leader's state directly. The leader's state considered is time varying. We present necessary and sufficient conditions for boundedness of the tracking(More)
In this paper, we study the distributed constrained optimization problem where the objective function is the sum of local convex cost functions of distributed nodes in a network, subject to a global inequality constraint. To solve this problem, we propose a consensus-based distributed regularized primal-dual subgradient method. In contrast to the existing(More)
In this brief, we consider the multiagent optimization over a network where multiple agents try to minimize a sum of nonsmooth but Lipschitz continuous functions, subject to a convex state constraint set. The underlying network topology is modeled as time varying. We propose a randomized derivative-free method, where in each update, the random gradient-free(More)
This paper considers the l 2-l ∞ filter problem for discrete time-delay Markovian jump neural networks. Attention is focused on the design of a reduced-order filter to guarantee stochastic stability and a prescribed l 2-l ∞ performance for the filtering error system. In terms of linear matrix inequalities (LMIs), a delay-dependent sufficient condition for(More)
This paper studies the problem of minimizing a sum of (possible nonsmooth) convex functions that are corresponding to multiple interacting nodes, subject to a convex state constraint set. Time-varying directed network is considered here. Two types of computational constraints are investigated in this paper: one where the information of gradients is not(More)