Tropp, Joel

Monograph from CaltechAUTHORS

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