58 citations · 99 across the 23 of their papers we have counts for
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A Flexible Gradient Tracking Algorithmic Framework for Decentralized Optimization
Albert S. Berahas, Raghu Bollapragada, Shagun Gupta
In decentralized optimization over networks, each node in the network has a portion of the global objective function and the aim is to collectively optimize this function. Gradient…
Adaptive Consensus: A network pruning approach for decentralized optimization
Suhail M. Shah, Albert S. Berahas, Raghu Bollapragada
We consider network-based decentralized optimization problems, where each node in the network possesses a local function and the objective is to collectively attain a consensus sol…
A Stochastic Gradient Tracking Algorithm for Decentralized Optimization With Inexact Communication
Suhail M. Shah, Raghu Bollapragada
Decentralized optimization is typically studied under the assumption of noise-free transmission. However, real-world scenarios often involve the presence of noise due to factors su…
An Adaptive Sampling Augmented Lagrangian Method for Stochastic Optimization with Deterministic Constraints
Raghu Bollapragada, Cem Karamanli, Brendan Keith +3
The primary goal of this paper is to provide an efficient solution algorithm based on the augmented Lagrangian framework for optimization problems with a stochastic objective funct…
Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized Optimization
Albert S. Berahas, Raghu Bollapragada, Shagun Gupta
Gradient tracking methods have emerged as one of the most popular approaches for solving decentralized optimization problems over networks. In this setting, each node in the networ…