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Distributed Stochastic Subgradient Projection Algorithms for Convex Optimization
S. Sundhar Ram, A. Nedich, V. V. Veeravalli
We consider a distributed multi-agent network system where the goal is to minimize a sum of convex objective functions of the agents subject to a common convex constraint set. Each…
Incremental Stochastic Subgradient Algorithms for Convex Optimization
S Sundhar Ram, A Nedich, V. V. Veeravalli
In this paper we study the effect of stochastic errors on two constrained incremental sub-gradient algorithms. We view the incremental sub-gradient algorithms as decentralized netw…
Distributed and Recursive Parameter Estimation in Parametrized Linear State-Space Models
S. Sundhar Ram, V. V. Veeravalli, A. Nedic
We consider a network of sensors deployed to sense a spatio-temporal field and estimate a parameter of interest. We are interested in the case where the temporal process sensed by…
Distributed Subgradient Methods and Quantization Effects
Angelia Nedić, Alex Olshevsky, Asuman Ozdaglar +1
We consider a convex unconstrained optimization problem that arises in a network of agents whose goal is to cooperatively optimize the sum of the individual agent objective functio…
Constrained Consensus
Angelia Nedić, Asuman Ozdaglar, Pablo A. Parrilo
We present distributed algorithms that can be used by multiple agents to align their estimates with a particular value over a network with time-varying connectivity. Our framework…