Steven Diamond

Ph.D. Candidate
Department of Computer Science

Stanford University

Advisor: Professor Stephen Boyd

CV

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. To Appear in Proceedings of CDC, 2016.

Stochastic Matrix-Free Equilibration. S. Diamond and S. Boyd. To Appear in Journal of Optimization Theory and Applications, 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. Journal of Machine Learning Research, 17(83):1-5, 2016.

Matrix-free Convex Optimization Modeling. S. Diamond and S. Boyd. To Appear in Optimization and Applications in Control and Data Sciences, Springer, 2016.

Disciplined Convex Stochastic Programming: A New Framework for Stochastic Optimization. A. Ali, Z. Kolter, S. Diamond, and S. Boyd. Proceedings of the 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. Proceedings of the Workshop for High Performance Technical Computing in Dynamic Languages, 2014.

CVXPY, a Python-embedded modeling language for convex optimization.

ProxImaL, a domain-specific language for image 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.

DCCP, a CVXPY extension for difference-of-convex programming.

NCVX, a CVXPY extension for heuristic solution of nonconvex problems.

Find more projects on my Github page.