Arvind Raghunathan

2 accepted papers

2025

Constrained Optimization From a Control Perspective via Feedback Linearization

Runyu Zhang, Arvind Raghunathan, Jeff S Shamma, Na Li

NeurIPS 2025poster

Tools from control and dynamical systems have proven valuable for analyzing and developing optimization methods. In this paper, we establish rigorous theoretical foundations for using feedback linearization—a well-established nonlinear control technique—to solve constrained optimization problems. Fo…

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2019

Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function

Arvind Raghunathan, Anoop Cherian, Devesh Jha

ICML 2019oral

Computing Nash equilibrium (NE) of multi-player games has witnessed renewed interest due to recent advances in generative adversarial networks. However, computing equilibrium efficiently is challenging. To this end, we introduce the Gradient-based Nikaido-Isoda (GNI) function which serves: (i) as a…

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