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Morteza Ashraphijuo
Morteza Ashraphijuo
Senior Applied Scientist at Uber
Verified email at columbia.edu - Homepage
Title
Cited by
Cited by
Year
Promises of conic relaxation for contingency-constrained optimal power flow problem
R Madani, M Ashraphijuo, J Lavaei
IEEE Transactions on Power Systems 31 (2), 1297-1307, 2016
2082016
Fundamental Conditions for Low-CP-Rank Tensor Completion
M Ashraphijuo, X Wang
Journal of Machine Learning Research (JMLR) 18 (63), 1-29, 2017
642017
Conic relaxations of the unit commitment problem
S Fattahi, M Ashraphijuo, J Lavaei, A Atamtürk
Energy 134 (2017), 1079-1095, 2017
602017
Deterministic and probabilistic conditions for finite completability of low-tucker-rank tensor
M Ashraphijuo, V Aggarwal, X Wang
IEEE Transactions on Information Theory 65 (9), 5380 - 5400, 2019
29*2019
A Characterization of Sampling Patterns for Low-Tucker-Rank Tensor Completion Problem
M Ashraphijuo, V Aggarwal, X Wang
Information Theory Proceedings (ISIT), 2017 IEEE International Symposium on …, 2017
262017
Rank determination for low-rank data completion
M Ashraphijuo, X Wang, V Aggarwal
Journal of Machine Learning Research (JMLR) 18 (98), 1-29, 2017
222017
Characterization of rank-constrained feasibility problems via a finite number of convex programs
M Ashraphijuo, R Madani, J Lavaei
2016 IEEE 55th Conference on Decision and Control (CDC), 6544-6550, 2016
212016
Power system state estimation with a limited number of measurements
R Madani, M Ashraphijuo, J Lavaei, R Baldick
2016 IEEE 55th conference on decision and control (CDC), 672-679, 2016
202016
Fundamental sampling patterns for low-rank multi-view data completion
M Ashraphijuo, X Wang, V Aggarwal
Pattern Recognition 103 (107307), 1-24, 2020
19*2020
On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion
M Ashraphijuo, V Aggarwal, X Wang
IEEE Signal Processing Letters 25 (3), 343-347, 2018
192018
A Characterization of Sampling Patterns for Low-Rank Multi-View Data Completion Problem
M Ashraphijuo, X Wang, V Aggarwal
Information Theory Proceedings (ISIT), 2017 IEEE International Symposium on …, 2017
192017
Characterization of sampling patterns for low-tt-rank tensor retrieval
M Ashraphijuo, X Wang
Annals of Mathematics and Artificial Intelligence 88 (8), 859-886, 2020
17*2020
A strong semidefinite programming relaxation of the unit commitment problem
M Ashraphijuo, S Fattahi, J Lavaei, A Atamtürk
2016 IEEE 55th conference on decision and control (CDC), 694-701, 2016
162016
Clustering a union of low-rank subspaces of different dimensions with missing data
M Ashraphijuo, X Wang
Pattern Recognition Letters 120, 31-35, 2019
142019
Low-rank data completion with very low sampling rate using Newton's method
M Ashraphijuo, X Wang, J Zhang
IEEE Transactions on Signal Processing 67 (7), 1849-1859, 2019
112019
OPF Solver
R Madani, M Ashraphijuo, J Lavaei
Published online at http://www. ee. columbia. edu/~ lavaei/Software. html, 2014
112014
An approximation of the CP-rank of a partially sampled tensor
M Ashraphijuo, X Wang, V Aggarwal
Allerton Conference on Communication, Control, and Computing (Allerton), 2017
102017
Inverse function theorem for polynomial equations using semidefinite programming
M Ashraphijuo, R Madani, J Lavaei
Decision and Control (CDC), 2015 IEEE 54th Conference on, 6589-6596, 2015
102015
Sdp solver of optimal power flow users manual
R Madani, M Ashraphijuo, J Lavaei
http://www.columbia.edu/~rm3122/OPF_Solver_Guide.pdf, 2015
102015
Union of low-rank tensor spaces: Clustering and completion
M Ashraphijuo, X Wang
Journal of Machine Learning Research 21 (69), 1-36, 2020
92020
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