Nakatasukasa, Yuji; Tropp, Joel A. (2021) Fast & accurate randomized algorithms for linear systems and eigenvalue problems Series.ACM Technical Reports; No. 2021-01;

Sun, Yiming; Guo, Yang et al. (2021) Tensor Random Projection for Low Memory Dimension Reduction arXiv;

Chen, Chi-Fang; Huang, Hsin-Yuan (Robert) et al. (2020) Quantum simulation via randomized product formulas: Low gate complexity with accuracy guarantees arXiv;

Lotz, Martin; Tropp, Joel A. (2020) Sharp phase transitions in Euclidian integral geometry Series.ACM Technical Reports; No. 2020-01;

Yurtsever, Alp; Tropp, Joel A. et al. (2019) Scalable Semidefinite Programming arXiv;

Kueng, Richard; Tropp, Joel A. (2019) Binary Component Decomposition. Part I: The Positive-Semidefinite Case arXiv;

Kueng, Richard; Tropp, Joel A. (2019) Binary component decomposition. Part II: The asymmetric case arXiv;

Tropp, Joel A.; Yurtsever, Alp et al. (2018) More practical sketching algorithms for low-rank matrix approximation Series.ACM Technical Reports; No. 2018-01;

Tropp, Joel A. (2018) Analysis of randomized block Krylov methods Series.ACM Technical Reports; No. 2018-02;

Tropp, Joel A.; Yurtsever, Alp et al. (2017) Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data Series.ACM Technical Reports; No. 2017-03;

Yurtsever, Alp; Udell, Madeleine et al. (2017) Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 20-22 April 2017 , Fort Lauderdale, FL (Submitted)

McCoy, Michael B.; Tropp, Joel A. (2017) The Achievable Performance of Convex Demixing Series.ACM Technical Reports; No. 2017-02;

Tropp, Joel A.; Yurtsever, Alp et al. (2017) Randomized Single-View Algorithms for Low-Rank Matrix Approximation Series.ACM Technical Reports; No. 2017-01;

Oymak, Samet; Tropp, Joel A. (2015) Universality laws for randomized dimension reduction, with applications arXiv;

Tropp, Joel A. (2015) Second-Order Matrix Concentration Inequalities arXiv;

Moarref, Rashad; Sharma, Ati S. et al. (2014) A foundation for analytical developments in the logarithmic region of turbulent channels arXiv;

Gittens, A.; Tropp, J. A. (2014) Error Bounds for Random Matrix Approximation Schemes Series.ACM Technical Reports; No. 2014-01;

Gittens, Alex A.; Tropp, Joel A. (2014) Tail Bounds for All Eigenvalues of a Sum of Random Matrices Series.ACM Technical Reports; No. 2014-02;

McCoy, Michael B.; Tropp, Joel A. (2013) The achievable performance of convex demixing arXiv;

Paulin, Daniel; Mackey, Lester et al. (2013) Deriving Matrix Concentration Inequalities from Kernel Couplings arXiv;

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; No. 2012-01;

Chen, Richard Y.; Gittens, Alex et al. (2011) The Masked Sample Covariance Estimator: An Analysis via Matrix Concentration Inequalities arXiv; (Submitted)

Probel, Clément J.; Tropp, Joel A. (2011) Large-Scales PCA with Sparsity Constraints Series.ACM Technical Reports; No. 2011-02;

Tropp, Joel A. (2011) User-friendly Tail Bounds for Matrix Martingales Series.ACM Technical Reports; No. 2011-01;

Tropp, Joel A. (2010) User-Friendly Tail Bounds for Sums of Random Matrices Series.ACM Technical Reports; No. 2010-01;

Halko, N.; Martinsson, P. G. et al. (2009) Finding Structure with Randomness: Stochastic Algorithms for Constructing Approximate matrix Decompositions Series.ACM Technical Reports; No. 2009-05;

Tropp, Joel A.; Wright, Stephen J. (2009) Computational Methods for Sparse Solution of Linear Inverse Problems Series.ACM Technical Reports; No. 2009-01;

Needell, D.; Tropp, J. A. (2008) CoSaMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples Series.ACM Technical Reports; No. 2008-01;

Tropp, Joel A. (2008) Column Subset Selection, Matrix Factorization, and Eigenvalue Optimization Series.ACM Technical Reports; No. 2008-02;

Tropp, Joel A.; Gilbert, Anna C. (2007) Signal Recovery from Random Measurements Via Orthogonal Matching Pursuit: The Gaussian Case Series.ACM Technical Reports; No. 2007-01;

Gilbert, A. C.; Strauss, M. J. et al. (2006) Algorithmic linear dimension reduction in the ℓ_1 norm for sparse vectors (Submitted)