IEEE Trans Neural Netw Learn Syst - Randomized gradient-free method for multiagent optimization over time-varying networks.

Tópicos

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{ medic(1828) order(1363) alert(1069) }
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{ cost(1906) reduc(1198) effect(832) }
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{ time(1939) patient(1703) rate(768) }
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Resumo

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 oracles are utilized instead of the subgradients (SGs). In contrast to the existing work, we do not require that agents are able to compute the SGs of their objective functions. We establish the convergence of the method to an approximate solution of the multiagent optimization problem within the error level depending on the smoothing parameter and the Lipschitz constant of each agent's objective function. Finally, a numerical example is provided to demonstrate the effectiveness of the method.

Resumo Limpo

brief consid multiag optim network multipl agent tri minim sum nonsmooth lipschitz continu function subject convex state constraint set under network topolog model time vari propos random derivativefre method updat random gradientfre oracl util instead subgradi sgs contrast exist work requir agent abl comput sgs object function establish converg method approxim solut multiag optim problem within error level depend smooth paramet lipschitz constant agent object function final numer exampl provid demonstr effect method

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