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math.OC2024
Almost Sure Convergence and Non-asymptotic Concentration Bounds for Stochastic Mirror Descent Algorithm
Anik Kumar Paul, Arun D Mahindrakar, Rachel K Kalaimani
This letter investigates the convergence and concentration properties of the Stochastic Mirror Descent (SMD) algorithm utilizing biased stochastic subgradients. We establish the al…
math.OC2024
Convergence Analysis of Stochastic Saddle Point Mirror Descent Algorithm -- A Projected Dynamical View Point
Anik Kumar Paul, Arun D Mahindrakar, Rachel K Kalaimani
Saddle point problems, ubiquitous in optimization, extend beyond game theory to diverse domains like power networks and reinforcement learning. This paper presents novel approaches…
cs.LG2024
Federated Learning: A Stochastic Approximation Approach
Srihari P, Anik Kumar Paul, Bharath Bhikkaji
This paper considers the Federated learning (FL) in a stochastic approximation (SA) framework. Here, each client trains a local model using its dataset and…