(orcid 0000-0003-1024-1791)
Nakatasukasa, Yuji; Tropp, Joel A. (2021) Fast & accurate randomized algorithms for linear systems and eigenvalue problems Series ACM Technical Reports, 2021-01. https://doi.org/10.7907/cmyh-va31
Sun, Yiming; Guo, Yang et al. (2021) Tensor Random Projection for Low Memory Dimension Reduction arXiv; https://doi.org/10.48550/arXiv.2105.00105
Chen, Chi-Fang; Huang, Hsin-Yuan (Robert) et al. (2020) Quantum simulation via randomized product formulas: Low gate complexity with accuracy guarantees arXiv; https://doi.org/10.48550/arXiv.2008.11751
Lotz, Martin; Tropp, Joel A. (2020) Sharp phase transitions in Euclidian integral geometry Series ACM Technical Reports, 2020-01. https://doi.org/10.7907/9rja-rh15
Yurtsever, Alp; Tropp, Joel A. et al. (2019) Scalable Semidefinite Programming arXiv; https://doi.org/10.48550/arXiv.1912.02949
Kueng, Richard; Tropp, Joel A. (2019) Binary Component Decomposition. Part I: The Positive-Semidefinite Case arXiv; https://doi.org/10.48550/arXiv.1907.13603
Kueng, Richard; Tropp, Joel A. (2019) Binary component decomposition. Part II: The asymmetric case arXiv; https://doi.org/10.48550/arXiv.1907.13602
Tropp, Joel A.; Yurtsever, Alp et al. (2018) More practical sketching algorithms for low-rank matrix approximation Series ACM Technical Reports, 2018-01. https://doi.org/10.7907/bb7w-ve61
Tropp, Joel A. (2018) Analysis of randomized block Krylov methods Series ACM Technical Reports, 2018-02. https://resolver.caltech.edu/CaltechAUTHORS:20210624-180721369
Tropp, Joel A.; Yurtsever, Alp et al. (2017) Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data Series ACM Technical Reports, 2017-03. https://doi.org/10.7907/QJE2-RP11
Yurtsever, Alp; Udell, Madeleine et al. (2017) Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage In: 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 20-22 April 2017, Fort Lauderdale, FL https://doi.org/10.48550/arXiv.1702.06838 (Submitted)
McCoy, Michael B.; Tropp, Joel A. (2017) The Achievable Performance of Convex Demixing Series ACM Technical Reports, 2017-02. https://doi.org/10.7907/4KWM-5N31
Tropp, Joel A.; Yurtsever, Alp et al. (2017) Randomized Single-View Algorithms for Low-Rank Matrix Approximation Series ACM Technical Reports, 2017-01. https://doi.org/10.7907/Z9HT2M9C
Oymak, Samet; Tropp, Joel A. (2015) Universality laws for randomized dimension reduction, with applications arXiv; https://doi.org/10.48550/arXiv.1511.09433
Tropp, Joel A. (2015) Second-Order Matrix Concentration Inequalities arXiv; https://doi.org/10.48550/arXiv.1504.05919
Moarref, Rashad; Sharma, Ati S. et al. (2014) A foundation for analytical developments in the logarithmic region of turbulent channels arXiv; https://doi.org/10.48550/arXiv.1409.6047
Gittens, A.; Tropp, J. A. (2014) Error Bounds for Random Matrix Approximation Schemes Series ACM Technical Reports, 2014-01. https://doi.org/10.7907/03an-qj61
Gittens, Alex A.; Tropp, Joel A. (2014) Tail Bounds for All Eigenvalues of a Sum of Random Matrices Series ACM Technical Reports, 2014-02. https://doi.org/10.7907/tz8n-h623
McCoy, Michael B.; Tropp, Joel A. (2013) The achievable performance of convex demixing arXiv; https://doi.org/10.48550/arXiv.1309.7478
Paulin, Daniel; Mackey, Lester et al. (2013) Deriving Matrix Concentration Inequalities from Kernel Couplings arXiv; https://doi.org/10.48550/arXiv.1305.0612
Chen, Richard Y.; Gittens, Alex A. et al. (2012) The Masked Sample Covariance Estimator: An Analysis via the Matrix Laplace Transform Series ACM Technical Reports, 2012-01. https://doi.org/10.7907/6rfh-ce56
Chen, Richard Y.; Gittens, Alex et al. (2011) The Masked Sample Covariance Estimator: An Analysis via Matrix Concentration Inequalities arXiv; https://doi.org/10.48550/arXiv.1109.1637 (Submitted)
Probel, Clément J.; Tropp, Joel A. (2011) Large-Scales PCA with Sparsity Constraints Series ACM Technical Reports, 2011-02. https://doi.org/10.7907/51g8-zc61
Tropp, Joel A. (2011) User-friendly Tail Bounds for Matrix Martingales Series ACM Technical Reports, 2011-01. https://doi.org/10.7907/62v9-yh77
Tropp, Joel A. (2010) User-Friendly Tail Bounds for Sums of Random Matrices Series ACM Technical Reports, 2010-01. https://doi.org/10.7907/A14X-R435
Halko, N.; Martinsson, P. G. et al. (2009) Finding Structure with Randomness: Stochastic Algorithms for Constructing Approximate matrix Decompositions Series ACM Technical Reports, 2009-05. https://doi.org/10.7907/PK8V-V047
Tropp, Joel A.; Wright, Stephen J. (2009) Computational Methods for Sparse Solution of Linear Inverse Problems Series ACM Technical Reports, 2009-01. https://doi.org/10.7907/QF0D-J303
Needell, D.; Tropp, J. A. (2008) CoSaMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples Series ACM Technical Reports, 2008-01. https://doi.org/10.7907/KE0N-TN13
Tropp, Joel A. (2008) Column Subset Selection, Matrix Factorization, and Eigenvalue Optimization Series ACM Technical Reports, 2008-02. https://doi.org/10.7907/82PQ-TF75
Tropp, Joel A.; Gilbert, Anna C. (2007) Signal Recovery from Random Measurements Via Orthogonal Matching Pursuit: The Gaussian Case Series ACM Technical Reports, 2007-01. https://doi.org/10.7907/EG9R-Y984
Gilbert, A. C.; Strauss, M. J. et al. (2006) Algorithmic linear dimension reduction in the ℓ_1 norm for sparse vectors https://doi.org/10.48550/arXiv.0608079 (Submitted)