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Greetings! I am Raghu Bollapragada (Vijaya Raghavendra Bollapragada), an Assistant Professor in the Operations Research and Industrial Engineering program at The University of Texas at Austin. I am also affiliated with the ODEN Institute for Computational Engineering & Sciences, Machine Learning Laboratory , and Center for Dynamics and Controls of Material: an NSF MRSEC at UT Austin.
My research focuses on designing algorithms for nonlinear optimization. My research include works in subfields such as constrained optimization, stochastic optimization, distributed optimization, and optimization algorithms for machine learning. As the scale and difficulty of optimization problems being posed increases in areas such as logistics, controls, machine learning, and computational physics, designing new age optimization algorithms becomes necessary to tackle these problems while taking full advantage of hardware and software tools available.
News !!!
News !!!
- March 2025
Our paper "A stochastic gradient tracking algorithm for decentralized optimization with inexact communication" is now published in IEEE Transaction on Automatic Control. DOI: 10.1109/23M1599379
- December 2024
Our work on "Central Finite-Difference Based Gradient Estimation Methods for Stochastic Optimization" was presented at Winter Simulation Conference 2024, Orlando, FL.
- November 2024
Our paper "Adaptive Consensus: A network pruning approach for decentralized optimization" is now published in SIAM Journal On Optimization. DOI: 10.1137/23M1599379
Our paper on adaptive sampling based negative-curvature approaches for solving unconstrained stochastic optimization is now available online. https://arxiv.org/pdf/2411.10378
Our paper on efficient mathematical programming approaches for optimal camera placement is now available online. https://arxiv.org/pdf/2411.17942
- October 2024
Our paper "Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized Optimization" is now published in Journal of Optimization Theory and Applications. DOI: 10.1007/s10957-024-02554-8
I am honored to have been elected Vice Chair of Nonlinear Optimization for the INFORMS Optimization Society (2024–2026).
Our research group organized multiple sessions and delivered talks at the 2024 INFORMS Annual Meeting. Congratulations to Shagun Gupta, Yash Kumar, and Jiahao Shi on their contributions!
- September 2024
Our paper "A Retrospective Approximation for Smooth Stochastic Optimization" is now published in Mathematics of Operations Research. DOI: 10.1287/moor.2022.0136
Congratulations Marissa Llamas!!! Won the best poster award for her poster on "Towards Adaptive Selection of Directions in Unconstrained Deterministic Derivative-Free Optimization" at the Texas Advanced Computing Center Symposium.
Marissa Llamas (Summer Undergraduate Research Student) presented poster at the STARS Conference in San Diego, CA.
- August 2024
Our paper "On the fast convergence of heavy ball momentum" is now published in IMA Journal of Numerical Analysis. DOI: 10.1093/imanum/drad033
Our paper on Hessian averaging that achieves provably (deterministic) superlinear convergence without increasing Hessian sample sizes is available online. https://arxiv.org/pdf/2408.07268
Congratulations Cem Karamanli!!! Successfully defended his thesis on "Design and Analysis of Adaptive Methods for Nonlinear Optimization". He will be joining Amazon.
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Support
My research is supported by an NSF grant DMS-2324643, Argonne National Laboratory (ANL) and Lawrence Livermore National Laboratory (LLNL).
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