Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization
The Journal of Machine Learning Research
Almost sure stability of networked control systems under exponentially bounded bursts of dropouts
Proceedings of the 14th international conference on Hybrid systems: computation and control
Randomized optimal consensus of multi-agent systems
Automatica (Journal of IFAC)
Communication-efficient algorithms for statistical optimization
The Journal of Machine Learning Research
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We present an algorithm that generalizes the randomized incremental subgradient method with fixed stepsize due to Nedić and Bertsekas [SIAM J. Optim., 12 (2001), pp. 109-138]. Our novel algorithm is particularly suitable for distributed implementation and execution, and possible applications include distributed optimization, e.g., parameter estimation in networks of tiny wireless sensors. The stochastic component in the algorithm is described by a Markov chain, which can be constructed in a distributed fashion using only local information. We provide a detailed convergence analysis of the proposed algorithm and compare it with existing, both deterministic and randomized, incremental subgradient methods.