Zobrazeno 1 - 10
of 262
pro vyhledávání: '"GRIGORI, LAURA"'
Autor:
Daas, Hussam Al, Ballard, Grey, Grigori, Laura, Kumar, Suraj, Rouse, Kathryn, Verite, Mathieu
In this article, we focus on the communication costs of three symmetric matrix computations: i) multiplying a matrix with its transpose, known as a symmetric rank-k update (SYRK) ii) adding the result of the multiplication of a matrix with the transp
Externí odkaz:
http://arxiv.org/abs/2409.11304
In this paper, we introduce a randomized algorithm for solving the non-symmetric eigenvalue problem, referred to as randomized Implicitly Restarted Arnoldi (rIRA). This method relies on using a sketch-orthogonal basis during the Arnoldi process while
Externí odkaz:
http://arxiv.org/abs/2407.03208
Autor:
Grigori, Laura, Timsit, Edouard
This paper introduces a randomized Householder QR factorization (RHQR). This factorization can be used to obtain a well conditioned basis of a vector space and thus can be employed in a variety of applications. The RHQR factorization of the input mat
Externí odkaz:
http://arxiv.org/abs/2405.10923
Autor:
Murray, Riley, Demmel, James, Mahoney, Michael W., Erichson, N. Benjamin, Melnichenko, Maksim, Malik, Osman Asif, Grigori, Laura, Luszczek, Piotr, Dereziński, Michał, Lopes, Miles E., Liang, Tianyu, Luo, Hengrui, Dongarra, Jack
Randomized numerical linear algebra - RandNLA, for short - concerns the use of randomization as a resource to develop improved algorithms for large-scale linear algebra computations. The origins of contemporary RandNLA lay in theoretical computer sci
Externí odkaz:
http://arxiv.org/abs/2302.11474
Randomized orthogonal projection methods (ROPMs) can be used to speed up the computation of Krylov subspace methods in various contexts. Through a theoretical and numerical investigation, we establish that these methods produce quasi-optimal approxim
Externí odkaz:
http://arxiv.org/abs/2302.07466
Autor:
Bharadwaj, Vivek, Malik, Osman Asif, Murray, Riley, Grigori, Laura, Buluc, Aydin, Demmel, James
We present a data structure to randomly sample rows from the Khatri-Rao product of several matrices according to the exact distribution of its leverage scores. Our proposed sampler draws each row in time logarithmic in the height of the Khatri-Rao pr
Externí odkaz:
http://arxiv.org/abs/2301.12584
We introduce two new approximation methods for the numerical evaluation of the long-range Coulomb potential and the approximation of the resulting high dimensional Two-Electron Integrals tensor (TEI) with long-range interactions arising in molecular
Externí odkaz:
http://arxiv.org/abs/2210.13069
Publikováno v:
Proceedings of the International Conference on Machine Learning, pp. 1564-1576. PMLR, 2023
This article introduces a novel structured random matrix composed blockwise from subsampled randomized Hadamard transforms (SRHTs). The block SRHT is expected to outperform well-known dimension reduction maps, including SRHT and Gaussian matrices, on
Externí odkaz:
http://arxiv.org/abs/2210.11295
Multiple Tensor-Times-Matrix (Multi-TTM) is a key computation in algorithms for computing and operating with the Tucker tensor decomposition, which is frequently used in multidimensional data analysis. We establish communication lower bounds that det
Externí odkaz:
http://arxiv.org/abs/2207.10437
Communication lower bounds have long been established for matrix multiplication algorithms. However, most methods of asymptotic analysis have either ignored the constant factors or not obtained the tightest possible values. Recent work has demonstrat
Externí odkaz:
http://arxiv.org/abs/2205.13407