Kiran Rokade

Papers

Total Citations

H-Index

About

Kiran Rokade is a researcher focused on the critical challenges of distributed optimization, particularly within the constraints of directed communication networks. Their work addresses a fundamental problem in modern networked systems—from power grids and robotics to statistical learning—where agents must coordinate without bidirectional links. Rokade’s major contribution lies in advancing the Alternating Direction Method of Multipliers (ADMM) for these realistic, unidirectional settings. Their most-cited paper, "Distributed ADMM With Linear Updates Over Directed Networks" (2025, 4 citations), proposes a novel algorithm that maintains convergence guarantees while using only linear updates, a significant improvement in computational efficiency. This builds on their earlier foundational work, "Distributed ADMM over directed networks" (2021, 3 citations), which established key theoretical frameworks. By enabling robust, decentralized optimization where communication is one-way, Rokade’s research has direct implications for scalable multi-agent systems, making their work essential reading for students and engineers tackling real-world coordination problems in complex, non-ideal network topologies.