Raghu Bollapragada

21 papers A* 1A 2Misc 1Journal 17
YearRankTypeTitle / Venue / Authors
2025 J jnl
A Retrospective Approximation Approach for Smooth Stochastic Optimization.
Math. Oper. Res.
David Newton, Raghu Bollapragada, Raghu Pasupathy, Nung Kwan Yip
2025 J jnl
A Stochastic Gradient Tracking Algorithm for Decentralized Optimization With Inexact Communication.
IEEE Trans. Autom. Control.
Suhail M. Shah, Raghu Bollapragada
2025 J jnl
A flexible gradient tracking algorithmic framework for decentralized optimization.
Comput. Optim. Appl.
Albert S. Berahas, Raghu Bollapragada, Shagun Gupta
2024 J jnl
Adaptive Consensus: A Network Pruning Approach for Decentralized Optimization.
SIAM J. Optim.
Suhail M. Shah, Albert S. Berahas, Raghu Bollapragada
2024 J jnl
Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized Optimization.
J. Optim. Theory Appl.
Albert S. Berahas, Raghu Bollapragada, Shagun Gupta
2024 Misc conf
Central Finite-Difference Based Gradient Estimation Methods for Stochastic Optimization.
WSC
Raghu Bollapragada, Cem Karamanli, Stefan M. Wild
2024 J jnl
Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods.
CoRR
Thomas O'Leary-Roseberry, Raghu Bollapragada
2023 J jnl
Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization.
Math. Program. Comput.
Raghu Bollapragada, Stefan M. Wild
2023 J jnl
An adaptive sampling augmented Lagrangian method for stochastic optimization with deterministic constraints.
Comput. Math. Appl.
Raghu Bollapragada, Cem Karamanli, Brendan Keith, Boyan S. Lazarov, Socratis Petrides, Jingyi Wang
2023 J jnl
Nonlinear acceleration of momentum and primal-dual algorithms.
Math. Program.
Raghu Bollapragada, Damien Scieur, Alexandre d'Aspremont
2022 A conf
Learning a neural Pareto manifold extractor with constraints.
UAI
Soumyajit Gupta, Gurpreet Singh, Raghu Bollapragada, Matthew Lease
2022 J jnl
On the fast convergence of minibatch heavy ball momentum.
CoRR
Raghu Bollapragada, Tyler Chen, Rachel A. Ward
2021 J jnl
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization.
CoRR
Raghu Bollapragada, Stefan M. Wild
2021 J jnl
On the Convergence of Nested Decentralized Gradient Methods With Multiple Consensus and Gradient Steps.
IEEE Trans. Signal Process.
Albert S. Berahas, Raghu Bollapragada, Ermin Wei
2020 J jnl
An investigation of Newton-Sketch and subsampled Newton methods.
Optim. Methods Softw.
Albert S. Berahas, Raghu Bollapragada, Jorge Nocedal
2019 J jnl
Balancing Communication and Computation in Distributed Optimization.
IEEE Trans. Autom. Control.
Albert S. Berahas, Raghu Bollapragada, Nitish Shirish Keskar, Ermin Wei
2019 A conf
Nonlinear Acceleration of Primal-Dual Algorithms.
AISTATS
Raghu Bollapragada, Damien Scieur, Alexandre d'Aspremont
2018 A* conf
A Progressive Batching L-BFGS Method for Machine Learning.
ICML
Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang
2018 J jnl
A Progressive Batching L-BFGS Method for Machine Learning.
CoRR
Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang
2018 J jnl
Adaptive Sampling Strategies for Stochastic Optimization.
SIAM J. Optim.
Raghu Bollapragada, Richard H. Byrd, Jorge Nocedal
2017 J jnl
An Investigation of Newton-Sketch and Subsampled Newton Methods.
CoRR
Albert S. Berahas, Raghu Bollapragada, Jorge Nocedal