Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Joel A Tropp"'
Publikováno v:
New Journal of Physics, Vol 17, Iss 5, p 053044 (2015)
Ptychography is a powerful computational imaging technique that transforms a collection of low-resolution images into a high-resolution sample reconstruction. Unfortunately, algorithms that currently solve this reconstruction problem lack stability,
Externí odkaz:
https://doaj.org/article/b411c124ab064f2b8aa8cdcf1b935cef
Publikováno v:
PRX Quantum, Vol 2, Iss 4, p 040305 (2021)
Quantum simulation has wide applications in quantum chemistry and physics. Recently, scientists have begun exploring the use of randomized methods for accelerating quantum simulation. Among them, a simple and powerful technique, called qDRIFT, is kno
Externí odkaz:
https://doaj.org/article/94e167d70b214cbebed2f716228b1656
Publikováno v:
Foundations of Computational Mathematics. 22:1767-1799
This paper develops nonasymptotic growth and concentration bounds for a product of independent random matrices. These results sharpen and generalize recent work of Henriksen-Ward, and they are similar in spirit to the results of Ahlswede-Winter and o
Autor:
Joel A. Tropp, Richard Kueng
Publikováno v:
SIAM Journal on Mathematics of Data Science. 3:544-572
This paper studies the problem of decomposing a low-rank positive-semidefinite matrix into symmetric factors with binary entries, either $\{\pm 1\}$ or $\{0,1\}$. This research answers fundamental questions about the existence and uniqueness of these
Autor:
Joel A. Tropp, Per-Gunnar Martinsson
Publikováno v:
Acta Numerica. 29:403-572
This survey describes probabilistic algorithms for linear algebraic computations, such as factorizing matrices and solving linear systems. It focuses on techniques that have a proven track record for real-world problems. The paper treats both the the
Publikováno v:
SIAM Journal on Mathematics of Data Science. 2:1123-1150
This paper describes a new algorithm for computing a low-Tucker-rank approximation of a tensor. The method applies a randomized linear map to the tensor to obtain a sketch that captures the important directions within each mode, as well as the intera
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision (ICCV).
Kernel analog forecasting (KAF) is a powerful methodology for data-driven, non-parametric forecasting of dynamically generated time series data. This approach has a rigorous foundation in Koopman operator theory and it produces good forecasts in prac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c282421dca3d93dfd89c732de13f8a3e
http://arxiv.org/abs/2109.09703
http://arxiv.org/abs/2109.09703
Autor:
De Huang, Joel A. Tropp
This paper deduces exponential matrix concentration from a Poincare inequality via a short, conceptual argument. Among other examples, this theory applies to matrix-valued functions of a uniformly log-concave random vector. The proof relies on the su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac4ce4f6b93c06fe026a56eff248b9b6
https://resolver.caltech.edu/CaltechAUTHORS:20201218-154430753
https://resolver.caltech.edu/CaltechAUTHORS:20201218-154430753
Autor:
Joel A. Tropp, De Huang
Publikováno v:
Electron. J. Probab.
Matrix concentration inequalities provide information about the probability that a random matrix is close to its expectation with respect to the $l_2$ operator norm. This paper uses semigroup methods to derive sharp nonlinear matrix inequalities. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dcb4ec86a8ebfa3df98b2e91a918ce65
https://projecteuclid.org/euclid.ejp/1610010034
https://projecteuclid.org/euclid.ejp/1610010034