Joel A. Tropp

California Institute of Technology — United States

Engineering · Physical Sciences

55h-index36.2kcitations197works5.42yr avg
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Research Topics

Sparse and Compressive Sensing Techniques(92), Matrix Theory and Algorithms(31), Stochastic Gradient Optimization Techniques(29), Blind Source Separation Techniques(28), Random Matrices and Applications(28)

Publications197 total

Embrace Rejection: Kernel Matrix Approximation by Accelerated Randomly Pivoted Cholesky
SIAM Journal on Matrix Analysis and Applications·2025Open Access
Faster Linear Algebra Algorithms with Structured Random Matrices
arXiv (Cornell University)·2025Open Access
Successive randomized compression: A randomized algorithm for the compressed MPO-MPS product
arXiv (Cornell University)·2025Open Access
Comparison theorems for the minimum eigenvalue of a random positive-semidefinite matrix
arXiv (Cornell University)·2025Open Access
Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations
Communications on Pure and Applied Mathematics·2024· 7 citedOpen Access
Embrace rejection: Kernel matrix approximation by accelerated randomly pivoted Cholesky
arXiv (Cornell University)·2024Open Access
Fast and Accurate Randomized Algorithms for Linear Systems and Eigenvalue Problems
SIAM Journal on Matrix Analysis and Applications·2024· 25 cited
A new approach to strong convergence
arXiv (Cornell University)·2024· 1 citedOpen Access
Sparse Random Hamiltonians Are Quantumly Easy
Physical Review X·2024· 6 citedOpen Access
Efficient Error and Variance Estimation for Randomized Matrix Computations
SIAM Journal on Scientific Computing·2024· 5 cited
XT<scp>race</scp>: Making the Most of Every Sample in Stochastic Trace Estimation
SIAM Journal on Matrix Analysis and Applications·2024· 15 cited
Randomized matrix computations: themes and variations
arXiv (Cornell University)·2023· 1 citedOpen Access
Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications
arXiv (Cornell University)·2023· 4 citedOpen Access
Randomized Nyström Preconditioning
SIAM Journal on Matrix Analysis and Applications·2023· 20 cited
Learning to Forecast Dynamical Systems from Streaming Data
SIAM Journal on Applied Dynamical Systems·2023· 8 cited
Robust, randomized preconditioning for kernel ridge regression
arXiv (Cornell University)·2023· 1 citedOpen Access
Sparse random Hamiltonians are quantumly easy
arXiv (Cornell University)·2023Open Access
XTrace: Making the most of every sample in stochastic trace estimation
arXiv (Cornell University)·2023· 4 citedOpen Access
Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations
arXiv (Cornell University)·2022· 4 citedOpen Access
Efficient error and variance estimation for randomized matrix computations
arXiv (Cornell University)·2022Open Access
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Frequent Co-authors

Anna C. Gilbert(8), Inderjit S. Dhillon(5), Deanna Needell(4), Robert W. Heath(4), Michael Strauss(4), Michael B. McCoy(3), Roman Vershynin(3), Per‐Gunnar Martinsson(2), Martin J. Strauss(2), Marco F. Duarte(2), Justin Romberg(2), Richard G. Baraniuk(2), Stephen J. Wright(2), Thomas Strohmer(2), Dennis Amelunxen(2), Martin Lötz(2), Susan J. Kohler(2), Yi‐Fen Yen(2), Jan Wolber(2), Mark J. Albers(2)