A rewriting system for convex optimization problems. - Akshay Agrawal 001; Robin Verschueren; Steven Diamond; Stephen P. Boyd (J. Control. Decis., 2018)

Differentiable Convex Optimization Layers. - Akshay Agrawal 001; Brandon Amos; Shane T. Barratt; Stephen P. Boyd; Steven Diamond; J. Zico Kolter (NeurIPS, 2019)

Disciplined geometric programming. - Akshay Agrawal 001; Steven Diamond; Stephen P. Boyd (Optim. Lett., 2019)

Learning Convex Optimization Control Policies. - Akshay Agrawal 001; Shane T. Barratt; Stephen P. Boyd; Bartolomeo Stellato (L4DC, 2020)

Disciplined quasiconvex programming. - Akshay Agrawal 001; Stephen P. Boyd (Optim. Lett., 2020)

Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP. - Deepak Narayanan; Fiodar Kazhamiaka; Firas Abuzaid; Peter Kraft; Akshay Agrawal 001; Srikanth Kandula; Stephen P. Boyd; Matei Zaharia (SOSP, 2021)

Minimum-Distortion Embedding. - Akshay Agrawal 001; Alnur Ali; Stephen P. Boyd (Found. Trends Mach. Learn., 2021)

Learning Convex Optimization Models. - Akshay Agrawal 001; Shane T. Barratt; Stephen P. Boyd (IEEE CAA J. Autom. Sinica, 2021)

Embedded Code Generation With CVXPY. - Maximilian Schaller; Goran Banjac; Steven Diamond; Akshay Agrawal 001; Bartolomeo Stellato; Stephen P. Boyd (IEEE Control. Syst. Lett., 2022)

Allocation of fungible resources via a fast, scalable price discovery method. - Akshay Agrawal 001; Stephen P. Boyd; Deepak Narayanan; Fiodar Kazhamiaka; Matei Zaharia (Math. Program. Comput., 2022)

Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models. - Ziheng Cheng; Junzi Zhang; Akshay Agrawal 001; Stephen P. Boyd (CoRR, 2023)

Computing tighter bounds on the n-queens constant via Newton's method. - Parth Nobel; Akshay Agrawal 001; Stephen P. Boyd (Optim. Lett., 2023)