| Distributed optimization and statistical learning via the alternating direction method of multipliers S Boyd, N Parikh, E Chu, B Peleato, J Eckstein Foundations and Trends in Machine Learning 3 (1), 1-122, 2011 | 17323 | 2011 |
| Proximal Algorithms N Parikh, S Boyd Foundations and Trends in Optimization 1 (3), 2014 | 3970 | 2014 |
| Conic optimization via operator splitting and homogeneous self-dual embedding B O’donoghue, E Chu, N Parikh, S Boyd Journal of Optimization Theory and Applications 169 (3), 1042-1068, 2016 | 501 | 2016 |
| Block Splitting for Distributed Optimization N Parikh, S Boyd Mathematical Programming Computation, 2013 | 127 | 2013 |
| SCS: Splitting conic solver, version 2.0. 2 B O’Donoghue, E Chu, N Parikh, S Boyd 2, 2017 | 104 | 2017 |
| Code Generation for Embedded Second-Order Cone Programming E Chu, N Parikh, A Domahidi, S Boyd European Control Conference, 0 | 56* | |
| A primal-dual operator splitting method for conic optimization E Chu, B O’Donoghue, N Parikh, S Boyd Stanford Internal Report, 2013 | 16 | 2013 |
| Matlab scripts for alternating direction method of multipliers S Boyd, N Parikh, E Chu, B Peleato, J Eckstein Technical Report, Technical Report, 2012 | 16 | 2012 |
| Block Splitting for Large-Scale Distributed Learning N Parikh, S Boyd | 10 | 2011 |
| NYC AI Primer N Parikh, A Hohman https://nparikh.org/publications/nyc_ai_primer/, 2021 | | 2021 |
| NYC AI Strategy N Parikh, A Hohman https://nparikh.org/publications/nyc_ai_strategy/, 2021 | | 2021 |
| Presented by: Anis Elgabli, Amir Daneshmand S Boyd, N Parikh, ECB Peleato, J Eckstein | | 2016 |
| Errata for ‘Proximal Algorithms’ N Parikh, S Boyd | | 2014 |
| LATEX Style Guide for EE 364B S Boyd, EK Ryu, N Parikh | | 2014 |
| Distributed Convex Optimization with Proximal Methods N Parikh Stanford University, 2014 | | 2014 |
| CS 228T: A BIRD’S EYE VIEW N PARIKH | | 2011 |
| Graphical Models for Primarily Unsupervised Sequence Labeling N Parikh, M Dredze | | 2007 |
| NOTES ON TYPES AND PROGRAMMING LANGUAGES N Parikh | | 2004 |
| Convex Optimization: from Real-Time Embedded to Large-Scale Distributed S Boyd, N Parikh, E Chu, Y Wang, J Mattingley | | |
| Using the Foundations and Trends LaTeX Macros N Parikh, S Boyd | | |
Stephen BoydProfessor of Electrical Engineering, Computer Science, and Management Science, StanfordVerified email at stanford.edu
