Published Articles
Published Articles
Berahas, A. S., Bollapragada, R., Gupta, S. A Flexible Gradient Tracking Algorithmic Framework for Decentralized Optimization. arXiv:2312.06814. Computational Optimization and Applications. DOI: 10.1007/s10589-025-00685-w
Shah, S., Bollapragada, R., (03/2025). A stochastic gradient tracking algorithm for decentralized optimization with inexact communication. IEEE Transactions on Automatic Control. DOI: 10.1109/TAC.2025.3548470
Shah, S., Berahas, A. S., Bollapragada, R., (11/2024). Adaptive Consensus: A network pruning approach for decentralized optimization. SIAM Journal on Optimization. DOI: 10.1137/23M1599379
Berahas, A. S., Bollapragada, R., Gupta, S., (10/2024). Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized Optimization. Journal of Optimization Theory and Applications, 1-34. DOI: 10.1007/s10957-024-02554-8
Newton, D., Bollapragada, R., Pasupathy, R., Yip, N. K., (09/2024). A Retrospective Approximation for Smooth Stochastic Optimization. Mathematics of Operations Research. DOI:10.1287/moor.2022.0136
Bollapragada, R., Chen, T., Ward, R., (08/2024). On the fast convergence of minibatch heavy ball momentum. IMA Journal of Numerical Analysis. DOI: 10.1093/imanum/drad033
Bollapragada, R., Karamanli, C., Keith, B., Lazarov, B., Petrides, S., (10/2023). Adaptive Sampling Augmented Lagrangian Methods for Stochastic Optimization. Computers & Mathematics with Applications, 149: 239-258. DOI: 10.1016/j.camwa.2023.09.014
Xie, Y., Bollapragada, R., Byrd R., Nocedal, J., (05/2023). Constrained and Composite Optimization via Adaptive Sampling Methods. IMA Journal of Numerical Analysis, 44(2): 680 – 709. DOI: 10.1093/imanum/drad020
Bollapragada, R., Wild, S.M., (03/2023). Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization. Mathematical Programming Computation, 15(2): 327 - 364. DOI: 10.1007/s12532-023-00233-9
Bollapragada, R., Scieur, D., D'Aspremont, A., (02/2022). Nonlinear Acceleration of Momentum and Primal-Dual Methods. Mathematical Programming, 198: 325 – 362. DOI: 10.1007/s10107-022-01775-x
Berahas, A. S., Bollapragada, R., Wei, E., (07/2021). On the Convergence of Nested Decentralized Gradient Methods with Multiple Consensus and Gradient Steps. IEEE Transactions on Signal Processing, 69: 4192 – 4203. DOI: 10.1109/TSP.2021.3094906
Bollapragada, R., Menickelly, M., Nazarewicz W., O'Neal J., Reinhard P.G., Wild, S.M., (12/2020). Optimization and Machine Learning Training Algorithms for Fitting Numerical Physics Models. Journal of Physics G: Nuclear and Particle Physics, 48(2). DOI: 10.1088/1361-6471/abd009
Berahas, A. S., Bollapragada, R., Nocedal, J., (05/2020). An Investigation of Newton-Sketch and Subsampled Newton Methods. Optimization Methods and Software, 35(4):661–680. DOI: 10.1080/10556788.2020.1725751
Berahas, A. S., Bollapragada, R., Keskar, N. S., Wei, E., (08/2019). Balancing Communication and Computation in Distributed Optimization. IEEE Transactions on Automatic Control, 64(8): 3141 – 3155. DOI: 10.1109/TAC.2018.2880407
Bollapragada, R., Byrd, R., Nocedal, J., (12/2018). Adaptive Sampling Strategies for Stochastic Optimization. SIAM Journal on Optimization, 28(4): 3312 - 3343. DOI: 10.1137/17M1154679
Bollapragada, R., Byrd, R., Nocedal, J., (04/2018). Exact and Inexact Subsampled Newton Methods for Optimization. IMA Journal of Numerical Analysis, 39(2): 545 – 578. DOI: 10.1093/imanum/dry009
Vairamuthu, R., Vijaya Raghavendra, B., Ramesh Babu, N., (11/2016). Kinematical Design of a Precision Cylindrical Grinder with Volumetric Error Modelling and Sensitivity Analysis. International Journal of Precision Technology, 6:193-215. DOI: 10.1504/IJPTECH.2016.079999
Bollapragada, R., Karamanli, C., Wild, S.M., (2024). Central Finite-Difference Based Gradient Estimation Methods for Stochastic Optimization. In the 2024 Winter Simulation Conference, Orlando, Florida. [IEEE]
Gupta, S., Singh, G., Bollapragada, R., Lease, M., (2022). Learning a Neural Pareto Manifold Extractor with Constraints. In The 38th Conference on Uncertainty in Artificial Intelligence, Eindhoven, Netherlands. [MLR press]
Bollapragada, R., Scieur, D., D’Aspremont, A., (2019). Nonlinear Acceleration of Primal-Dual Algorithms. In proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), Okinawa, Japan, PMLR 89: 739 - 747, 2019. [MLR press]
Bollapragada, R., Mudigere, D., Nocedal, J., Shi, H.J.M., Tang, P.T.P., (2018). A Progressive Batching L-BFGS Method for Machine Learning. In proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, PMLR 80: 620 - 629. [MLR press]
Berahas, A. S., Bollapragada, R., Dong, W. Exploiting Negative Curvature in Conjunction with Adaptive Sampling: Theoretical Results and a Practical Algorithm. arXiv:2411.10378. (Status: First Revision, Computational Optimization and Applications)
O'Leary-Roseberry, T., Bollapragada, R. Fast Finite-Sum Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods. arXiv:2408.07268. (Status: First Revision, Mathematical Programming)
Berahas, A. S., Bollapragada, R., Shi, J. Modified Line Search Sequential Quadratic Methods for Equality-Constrained Optimization with Unified Global and Local Convergence Guarantees. arXiv:2406.11144. (Status: First Revision, SIAM Journal on Optimization)
Berahas, A. S., Bollapragada, R., Zhou, B. An Adaptive Sampling Sequential Quadratic Programming Method for Equality Constrained Stochastic Optimization. arXiv:2206.00712. (Status: First Revision, Mathematics of Operations Research)
Bollapragada, R., Karamanli, C., Wild, S.M. Derivative-Free Optimization via Adaptive Sampling Strategies. arXiv:2404.11893.(Status: Minor Revision, Optimization Methods and Software)
Kumar, Y., Bollapragada, R., Leibowicz, B. D. Efficient mathematical programming formulation and algorithmic framework for optimal camera placement. arXiv:2411.17942. (Status: Submitted, Annals of Operations Research)
Bollapragada, R., Karamanli, C. On the Convergence and Complexity of the Stochastic Central Finite-Difference Based Gradient Estimation Methods. arXiv:2501.06610. (Status: Submitted, Journal of Optimization Theory and Applications)
Berahas, A. S., Bollapragada, R., Gupta, S. Retrospective Approximation Sequential Quadratic Programming for Stochastic Optimization with General Deterministic Nonlinear Constraints arXiv:2505.19382. (Status: Submitted, Mathematical Programming)
Bollapragada, R., Gupta, S. On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under AdaptiveGradient Estimation arXiv:2507.14479. (Status: Submitted)
Berahas, A. S., Bollapragada, R., Karamanli, C., Shah, S. Adaptive Asynchronous Gradient Tracking Algorithms for Decentralized Optimization.
O'Leary-Roseberry, T., Bollapragada, R. Efficient Hessian averaging and adaptive gradients sampling approaches for stochastic optimization.
Berahas, A. S., Bollapragada, R., Wang, W. Adaptive Sampling Strategies for Stochastic Decentralized Optimization.
Bollapragada, R., Pasupathy, R, Gupta, A. Yip, N., K. Adaptive Fixed-Step Stochastic Gradient Method.
Wang, J., Bollapragada, R., Aravena, I., Petra, C. Stochastic sequential quadratic programming for a group of constrained nonsmooth optimization problems.
Berahas, A. S., Bollapragada, R., Dong, W. Negative Curvature Methods with High Probability Complexity Bounds for Stochastic Nonconvex Optimization.
Bollapragada, R., Kumar, Y. Stochastic Second-order Inexact Augmented Lagrangian Method for Nonconvex Expectation Constrained Optimization.
Bollapragada, R., Gupta, S. On the Convergence and Complexity of Proximal Gradient and Proximal Accelerated Gradient methods under Adaptive Sampling Strategies.
Bollapragada, V. R. Methods for Deterministic and Stochastic Optimization. PhD Thesis, Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA.
Bollapragada, V. R. An Approach for Volumetric Accuracy Analysis of Cylindrical Grinding Machine Based on Geometric Error Model. Masters Thesis, Department of Mechanical Engineering, I.I.T Madras, India.