Ruchi Tripathi

Papers

Total Citations

111

H-Index

About

Ruchi Tripathi is a leading researcher in optimization theory, with a primary focus on time-varying and online learning problems. Her work addresses fundamental challenges in dynamic optimization, particularly for nondifferentiable functions that arise in robotics, subspace tracking, and real-time estimation. Tripathi’s major contribution is the development of Inexact Proximal Online Gradient Descent (IPOGD) algorithms, which provide low-complexity solutions for problems where computational constraints and time-varying objectives demand efficient, approximate updates rather than exact solutions. Her most-cited paper, “Online Learning With Inexact Proximal Online Gradient Descent Algorithms” (2019, 109 citations), demonstrates the power of these methods for nondifferentiable dynamic optimization, balancing accuracy with computational feasibility. In her earlier work, “Time Varying optimization via Inexact Proximal Online Gradient Descent” (2018), she formalized the framework for minimizing functions with both differentiable and non-differentiable components, a common scenario in regularized learning problems. Tripathi’s research has significant implications for real-world systems requiring rapid adaptation, such as autonomous navigation and signal processing. Her work is essential reading for researchers and students exploring online convex optimization, proximal methods, and time-varying systems.