Raghavendra Bollapragada

About

Raghavendra Bollapragada is an Assistant Professor in the Department of Research at The University of Texas at Austin. His research areas include Analytics and Probabilistic Modeling. His work involves developing and analyzing optimization methods, including Newton-Sketch and subsampled Newton methods, as well as nonlinear acceleration of primal-dual algorithms. He has contributed to the field through various publications on stochastic optimization, distributed optimization, and machine learning, focusing on improving computational efficiency and balancing communication and computation in large-scale systems.

Research topics

• Computer Science
• Algorithm
• Mathematical optimization
• Mathematics
• Mathematical analysis
• Applied mathematics
• Economics

Selected publications

• Nonlinear acceleration of momentum and primal-dual algorithms

  Mathematical Programming · 2022 · 16 citations

  1st authorCorresponding
  DOI

• Constrained and composite optimization via adaptive sampling methods

  IMA Journal of Numerical Analysis · 2023-05-12 · 13 citations

  articleOpen access

  Abstract The motivation for this paper stems from the desire to develop an adaptive sampling method for solving constrained optimization problems, in which the objective function is stochastic and the constraints are deterministic. The method proposed in this paper is a proximal gradient method that can also be applied to the composite optimization problem min $f(x) + h(x)$, where $f$ is stochastic and $h$ is convex (but not necessarily differentiable). Adaptive sampling methods employ a mechani…

  PublisherOA PDFDOI

• On the Convergence of Nested Decentralized Gradient Methods with Multiple Consensus and Gradient Steps

  IEEE Transactions on Signal Processing · 2020 · 13 citations

  In this paper, we consider minimizing a sum of local convex objective functions in a distributed setting, where the cost of communication and/or computation can be expensive. We extend and generalize the analysis for a class of nested gradient-based distributed algorithms (NEAR-DGD; Berahas, Bollapragada, Keskar and Wei, 2018) to account for multiple gradient steps at every iteration. We show the effect of performing multiple gradient steps on the rate of convergence and on the size of the neigh…

  DOI

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

  Computers & Mathematics with Applications · 2023-10-02 · 11 citations

  articleOpen access1st author
  PublisherOA PDFDOI

• On the fast convergence of minibatch heavy ball momentum

  IMA Journal of Numerical Analysis · 2024-08-08 · 5 citations

  articleOpen access1st authorCorresponding

  Abstract Simple stochastic momentum methods are widely used in machine learning optimization, but their good practical performance is at odds with an absence of theoretical guarantees of acceleration in the literature. In this work, we aim to close the gap between theory and practice by showing that stochastic heavy ball momentum retains the fast linear rate of (deterministic) heavy ball momentum on quadratic optimization problems, at least when minibatching with a sufficiently large batch size.…

  PublisherOA PDFDOI

Frequent coauthors

• Albert S. Berahas

  University of Michigan–Ann Arbor

  15 shared

• Jorge Nocedal

  13 shared

• Stefan M. Wild

  Lawrence Berkeley National Laboratory

  8 shared

• Richard Byrd

  University of Colorado Boulder

  7 shared

• Tyler Chen

  4 shared

• Ermin Wei

  Northwestern University

  4 shared

• Raghu Pasupathy

  4 shared

• Ping Tang

  Chinese Academy of Sciences

  3 shared

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