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of 529
pro vyhledávání: '"Randomized numerical linear algebra"'
Autor:
Lavaee, Alex
We present Sketch 'n Solve, an open-source Python package that implements efficient randomized numerical linear algebra (RandNLA) techniques for solving large-scale least squares problems. While sketch-and-solve algorithms have demonstrated theoretic
Externí odkaz:
http://arxiv.org/abs/2409.14309
Large matrices arise in many machine learning and data analysis applications, including as representations of datasets, graphs, model weights, and first and second-order derivatives. Randomized Numerical Linear Algebra (RandNLA) is an area which uses
Externí odkaz:
http://arxiv.org/abs/2406.11151
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
Autor:
Puiu, Constantin Octavian
K-FAC is a successful tractable implementation of Natural Gradient for Deep Learning, which nevertheless suffers from the requirement to compute the inverse of the Kronecker factors (through an eigen-decomposition). This can be very time-consuming (o
Externí odkaz:
http://arxiv.org/abs/2206.15397
Autor:
Hesslow, Daniel, Cappelli, Alessandro, Carron, Igor, Daudet, Laurent, Lafargue, Raphaël, Müller, Kilian, Ohana, Ruben, Pariente, Gustave, Poli, Iacopo
Randomized Numerical Linear Algebra (RandNLA) is a powerful class of methods, widely used in High Performance Computing (HPC). RandNLA provides approximate solutions to linear algebra functions applied to large signals, at reduced computational costs
Externí odkaz:
http://arxiv.org/abs/2104.14429
We create classical (non-quantum) dynamic data structures supporting queries for recommender systems and least-squares regression that are comparable to their quantum analogues. De-quantizing such algorithms has received a flurry of attention in rece
Externí odkaz:
http://arxiv.org/abs/2011.04125
Randomized Numerical Linear Algebra (RandNLA) uses randomness to develop improved algorithms for matrix problems that arise in scientific computing, data science, machine learning, etc. Determinantal Point Processes (DPPs), a seemingly unrelated topi
Externí odkaz:
http://arxiv.org/abs/2005.03185
The statistical analysis of Randomized Numerical Linear Algebra (RandNLA) algorithms within the past few years has mostly focused on their performance as point estimators. However, this is insufficient for conducting statistical inference, e.g., cons
Externí odkaz:
http://arxiv.org/abs/2002.10526
Autor:
Martinsson, Per-Gunnar, Tropp, Joel
This survey describes probabilistic algorithms for linear algebra computations, such as factorizing matrices and solving linear systems. It focuses on techniques that have a proven track record for real-world problem instances. The paper treats both
Externí odkaz:
http://arxiv.org/abs/2002.01387
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