Computing tighter bounds on the n-Queen's Constant via Newton's Method. [bibtex][code]

P. Nobel, A. Agrawal, and S. Boyd. Optimization Letters.

Allocation of fungible resources via a fast, scalable price discovery method. [bibtex][code]

A. Agrawal, S. Boyd, D. Narayanan, F. Kazhamiaka, M. Zaharia. Mathematical Programming Computation.

Embedded code generation with CVXPY. [bibtex][code]

M. Schaller, G. Banjac, S. Diamond, A. Agrawal, B. Stellato, S. Boyd. IEEE Control Systems Letters.

Minimum-distortion embedding. [bibtex][slides][code]

A. Agrawal, A. Ali, and S. Boyd. Foundations and Trends in Machine Learning.

Constant function market makers: Multi-asset trades via convex optimization. [bibtex]

G. Angeris, A. Agrawal, A. Evans, T. Chitra, and S. Boyd. Pre-print.

Learning convex optimization models. [bibtex][code]

A. Agrawal, S. Barratt, and S. Boyd.* IEEE/CAA Journal of Automatica Sinica.

Differentiating through log-log convex programs. [bibtex][poster][code]

A. Agrawal and S. Boyd. Pre-print.

Learning convex optimization control policies. [bibtex][code]

A. Agrawal, S. Barratt, S. Boyd, B. Stellato.* Learning for Dynamics and Control (L4DC), oral presentation.

Disciplined quasiconvex programming. [bibtex][code]

A. Agrawal and S. Boyd. Optimization Letters.

Differentiable convex optimization layers. [bibtex][code][blog post]

A. Agrawal, B. Amos, S. Barratt, S. Boyd, S. Diamond, and J. Z. Kolter.* In Advances in Neural Information Processing Systems (NeurIPS).

Presented at the TensorFlow Developer Summit 2020, Sunnyvale [slides][video]

Differentiating through a cone program. [bibtex][code]

A. Agrawal, S. Barratt, S. Boyd, E. Busseti, W. Moursi.* Journal of Applied and Numerical Optimization.

TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learning. [bibtex][slides][blog post][code]

A. Agrawal, A. N. Modi, A. Passos, A. Lavoie, A. Agarwal, A. Shankar, I. Ganichev, J. Levenberg, M. Hong, R. Monga, S. Cai.* Systems for Machine Learning (SysML).

Disciplined geometric programming. [bibtex][tutorial][poster][code]

A. Agrawal, S. Diamond, S. Boyd. Optimization Letters.

Presented at ICCOPT 2019, Berlin [slides]

A rewriting system for convex optimization problems. [bibtex][slides][code]

A. Agrawal, R. Verschueren, S. Diamond, S. Boyd. Journal of Control and Decision.

YouEDU: Addressing confusion in MOOC discussion forums by recommending instructional video clips. [bibtex][dataset][code]

A. Agrawal, J. Venkatraman, S. Leonard, and A. Paepcke. Educational Data Mining.

Presented at EDM 2015, Madrid [slides]