Asynchronous Broadcast-Based Convex Optimization Over a Network

We consider a distributed multi-agent network system where each agent has its own convex objective function, which can be evaluated with stochastic errors. The problem consists of minimizing the sum of the agent functions over a commonly known constraint set, but without a central coordinator and wi...

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Vydané v:IEEE transactions on automatic control Ročník 56; číslo 6; s. 1337 - 1351
Hlavný autor: Nedic, A
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.06.2011
Institute of Electrical and Electronics Engineers
Predmet:
Applied sciences
Artificial intelligence
Asynchronous algorithms
Clocks
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Convergence
convex optimization
distributed multi-agent system
Exact sciences and technology
Markov processes
Optimization
random broadcast network
Sensors
Software
Symmetric matrices
Almost sure convergence
Probabilistic approach
distributed multi-agent system
Bounded error
Interconnected power system
Error bound
Network management
Distributed system
Communication network
Set constraint
Convex programming
random broadcast network
Value function
Multiagent system
Asynchronous algorithms
Broadcasting
Wireless network
convex optimization
Convex function
Objective function
Artificial intelligence
Asynchronous transmission
ISSN:0018-9286, 1558-2523
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Popis
Shrnutí:We consider a distributed multi-agent network system where each agent has its own convex objective function, which can be evaluated with stochastic errors. The problem consists of minimizing the sum of the agent functions over a commonly known constraint set, but without a central coordinator and without agents sharing the explicit form of their objectives. We propose an asynchronous broadcast-based algorithm where the communications over the network are subject to random link failures. We investigate the convergence properties of the algorithm for a diminishing (random) stepsize and a constant stepsize, where each agent chooses its own stepsize independently of the other agents. Under some standard conditions on the gradient errors, we establish almost sure convergence of the method to an optimal point for diminishing stepsize. For constant stepsize, we establish some error bounds on the expected distance from the optimal point and the expected function value. We also provide numerical results.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2010.2079650