3 citations · 4 across the 7 of their papers we have counts for
9 papers
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…
Making Simulated Annealing Sample Efficient for Discrete Stochastic Optimization
Suhail M. Shah
We study the regret of simulated annealing (SA) based approaches to solving discrete stochastic optimization problems. The main theoretical conclusion is that the regret of the sim…
Dynamic social learning under graph constraints
Konstantin Avrachenkov, Vivek S. Borkar, Sharayu Moharir +1
We introduce a model of graph-constrained dynamic choice with reinforcement modeled by positively -homogeneous rewards. We show that its empirical process, which can be written…
Linearly Convergent Asynchronous Distributed ADMM via Markov Sampling
Suhail M. Shah, Konstantin E. Avrachenkov
We consider the consensual distributed optimization problem and propose an asynchronous version of the Alternating Direction Method of Multipliers (ADMM) algorithm to solve it. The…
Frank-Wolfe variants for minimization of a sum of functions
Suhail M Shah
We propose several variants of the Frank-Wolfe algorithm to minimize a sum of functions. The main proposed algorithm is inspired from the dual averaging scheme of Nesterov adapted…
Stochastic Approximation on Riemannian manifolds
Suhail M. Shah
The standard theory of stochastic approximation (SA) is extended to the case when the constraint set is a Riemannian manifold. Specifically, the standard ODE method for analyzing S…
Distributed Optimization on Riemannian Manifolds for multi-agent networks
Suhail M. Shah
We consider the consensual distributed optimization problem in the Riemannian context. Specifically, the minimization of a sum of functions form is studied where each individual fu…
Distributed Stochastic Approximation with Local Projections
Suhail M. Shah, Vivek S. Borkar
We propose a distributed version of a stochastic approximation scheme constrained to remain in the intersection of a finite family of convex sets. The projection to the intersectio…