Raghu Bollapragada

According to our database, Raghu Bollapragada authored at least 11 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

An adaptive sampling augmented Lagrangian method for stochastic optimization with deterministic constraints.

Raghu Bollapragada, Cem Karamanli, Brendan Keith, Boyan Lazarov, Socratis Petrides, Jingyi Wang

Comput. Math. Appl., November, 2023

Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization.

Raghu Bollapragada, Stefan M. Wild

Math. Program. Comput., June, 2023

Nonlinear acceleration of momentum and primal-dual algorithms.

Raghu Bollapragada, Damien Scieur, Alexandre d'Aspremont

Math. Program., March, 2023

2022

On the fast convergence of minibatch heavy ball momentum.

Raghu Bollapragada, Tyler Chen, Rachel A. Ward

CoRR, 2022

Learning a neural Pareto manifold extractor with constraints.

Soumyajit Gupta, Gurpreet Singh, Raghu Bollapragada, Matthew Lease

Proceedings of the Uncertainty in Artificial Intelligence, 2022

2021

On the Convergence of Nested Decentralized Gradient Methods With Multiple Consensus and Gradient Steps.

Albert S. Berahas, Raghu Bollapragada, Ermin Wei

IEEE Trans. Signal Process., 2021

2020

An investigation of Newton-Sketch and subsampled Newton methods.

Albert S. Berahas, Raghu Bollapragada, Jorge Nocedal

Optim. Methods Softw., 2020

2019

Balancing Communication and Computation in Distributed Optimization.

Albert S. Berahas, Raghu Bollapragada, Nitish Shirish Keskar, Ermin Wei

IEEE Trans. Autom. Control., 2019

Nonlinear Acceleration of Primal-Dual Algorithms.

Raghu Bollapragada, Damien Scieur, Alexandre d'Aspremont

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Adaptive Sampling Strategies for Stochastic Optimization.

Raghu Bollapragada, Richard H. Byrd, Jorge Nocedal

SIAM J. Optim., 2018

A Progressive Batching L-BFGS Method for Machine Learning.

Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang

Proceedings of the 35th International Conference on Machine Learning, 2018