| General heuristics for nonconvex quadratically constrained quadratic programming J Park, S Boyd arXiv preprint arXiv:1703.07870, 2017 | 205 | 2017 |
| A semidefinite programming method for integer convex quadratic minimization J Park, S Boyd Optimization Letters, 2017 | 71 | 2017 |
| Concave quadratic cuts for mixed-integer quadratic problems J Park, S Boyd arXiv preprint arXiv:1510.06421, 2015 | 6 | 2015 |
| Lecture notes: Subgradient methods S Boyd, L Xiao, A Mutapcic January, 2014 | 6 | 2014 |
| Sparsity-preserving difference of positive semidefinite matrix representation of indefinite matrices J Park arXiv preprint arXiv:1609.06762, 2016 | 3 | 2016 |
| Deep Networks on the GPU M Harris, J Park, J Wang | 1 | 2009 |
| Approximation techniques for mixed-integer quadratic problems J Park Stanford University, 2017 | | 2017 |
| Symbolic Subdifferentiation in Python M Caló, J Park | | 2011 |
| Cortically Inspired Architectures for Action Recognition in Movie Clips D Kamm, J Park | | 2010 |
| Unsupervised Learning of Invariances in Deep Networks M Harris, D Kamm, J Park, J Wang | | |
Stephen BoydProfessor of Electrical Engineering, Computer Science, and Management Science, StanfordVerified email at stanford.edu