Email: diamond [at] cs (dot) stanford (dot) edu
Office: Packard 243, 350 Serra Mall, Stanford, CA 94305
ProxImaL: Efficient Image Optimization Using Proximal Algorithms. F. Heide, S. Diamond, M. Niessner, J. Ragan-Kelley, W. Heidrich, and G. Wetzstein. Proceedings of ACM SIGGRAPH, 2016.
Disciplined Convex-Concave Programming. X. Shen, S. Diamond, Y. Gu, and S. Boyd. Working Draft, 2016.
Stochastic Matrix-Free Equilibration. S. Diamond and S. Boyd. Working Draft, 2016.
A General System for Heuristic Solution of Convex Problems over Nonconvex Sets. S. Diamond, R. Takapoui, and S. Boyd. Working Draft, 2016.
Convex Optimization with Abstract Linear Operators. S. Diamond and S. Boyd. Proceedings of ICCV, 2015.
CVXPY: A Python-Embedded Modeling Language for Convex Optimization. S. Diamond and S. Boyd. To appear in JMLR MLOSS, 2016.
Matrix-free Convex Optimization Modeling. S. Diamond and S. Boyd. Working Draft, 2015.
Disciplined Convex Stochastic Programming: A New Framework for Stochastic Optimization. A. Ali, Z. Kolter, S. Diamond, and S. Boyd. Proceedings of the 31st Conference on Uncertainty in Artifial Intelligence, 2015.
SnapVX: A Network-Based Convex Optimization Solver. D. Hallac, C. Wong, S. Diamond, R. Sosic, S. Boyd, and J. Leskovec. Working Draft, 2015.
Convex Optimization in Julia. M. Udell, K. Mohan, D. Zeng, J. Hong, S. Diamond, and S. Boyd. SC14 Workshop on High Performance Technical Computing in Dynamic Languages, 2014.
CVXPY, a Python-embedded modeling language for convex optimization.
dcp.stanford.edu, an online visualization tool for disciplined convex programming.
A matrix-free version of CVXPY to accompany the paper “Matrix-free Convex Optimization Modeling”.
A matrix-free version of SCS to accompany the paper “Matrix-free Convex Optimization Modeling”.
A matrix-free version of POGS to accompany the paper “Convex Optimization with Abstract Linear Operators”.
A package that integrates matrix-free CVXPY and the matrix-free solvers.
Find more projects on my Github page.