npparikh [at-sign] cs.stanford.edu
TA: EE 364b: Convex Optimization II, Advanced Topics (Spring 2013-2014)
TA: CVX 101 (MOOC): Convex Optimization I (Winter 2013-2014)
Guest Instructor: EE 364a: Convex Optimization I (Winter 2012-2013)
Instructor: EE 364a: Convex Optimization I (Summer 2011-2012)
TA: EE 364a: Convex Optimization I (Winter 2011-2012)
TA: CS 228T: Probabilistic Graphical Models, Theoretical Foundations (Spring 2010-2011)
Google Scholar lists papers by citation count order. Papers are listed below by year of submission before they are published, or year of publication.
B. O'Donoghue, E. Chu, N. Parikh, and S. Boyd. Operator splitting for conic optimization via homogeneous self-dual embedding. Journal of Optimization Theory and Applications 169(3):1042-1068, 2016.
N. Parikh. Distributed Convex Optimization with Proximal Methods. Ph.D. thesis, Stanford University, 2014.
E. Chu, B. O'Donoghue, N. Parikh, and S. Boyd. A primal-dual operator splitting method for conic optimization. Working draft, 2014.
N. Parikh and S. Boyd. Proximal algorithms. Foundations and Trends in Optimization, volume 1, issue 3, pp. 127-239, 2014.
N. Parikh and S. Boyd. Block splitting for distributed optimization. Mathematical Programming Computation, volume 6, issue 1, pp. 77-102, 2014.
E. Chu, N. Parikh, A. Domahidi, and S. Boyd. Code generation for embedded second-order cone programming. European Control Conference, 2013.
N. Parikh and S. Boyd. Block splitting for large-scale distributed learning. Neural Information Processing Systems (NIPS), Workshop on Big Learning, 2011.
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, volume 3, issue 1, pp. 1-122, 2011.