Výsledky vyhledávání - "Large matrices"

A practical streaming approximate matrix multiplication algorithm

Autor: Kumudha Raimond, Deena P. Francis

Publikováno v: Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 1, Pp 1455-1465 (2022)

Approximate Matrix Multiplication (AMM) has emerged as a useful and computationally inexpensive substitute for actual multiplication of large matrices. Randomized as well as deterministic solutions to AMM were provided in the past. The latest work pr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3e4dfa36c3512ccdc75a2a272281a68d
http://www.sciencedirect.com/science/article/pii/S1319157818306396

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Residual and Restarting in Krylov Subspace Evaluation of the $\varphi$ Function

Autor: M. A. Botchev, Eugene E. Tyrtyshnikov, Leonid Knizhnerman

Publikováno v: SIAM Journal on Scientific Computing. 43:A3733-A3759

An efficient Krylov subspace algorithm for computing actions of the $\varphi$ matrix function for large matrices is proposed. This matrix function is widely used in exponential time integration, Ma...

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a20645d814b1d031a0cc492ce0981b9e
https://doi.org/10.1137/20m1375383

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A New and Simple Condensation Method of Computing Determinants of Large Matrices and Solving Large Linear Systems

Autor: Okoh Ufuoma

Publikováno v: Asian Research Journal of Mathematics. :1-13

The chief object of this work present a new and simple condensation method of finding determinants of large matrices and solving large linear systems.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c5a441e261fab3e47a43fe774445e327
https://doi.org/10.9734/arjom/2019/v15i430159

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A Robust Power Flow Algorithm Based on Bulirsch–Stoer Method

Autor: Marcos Tostado-Véliz, Francisco Jurado, Salah Kamel

Publikováno v: IEEE Transactions on Power Systems. 34:3081-3089

In this paper, we address the load-flow (LF) problem of very large scale systems. These types of systems show a very narrow region of attraction, and most of LF solvers tend to fail when a flat initial guess point is used. On the other hand, the solu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9da7d67947d3390d730d868ba6ec4f85
https://doi.org/10.1109/tpwrs.2019.2900513

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Asymptotic spectra of large matrices coming from the symmetrization of Toeplitz structure functions and applications to preconditioning

Autor: Paola Ferrari, Nikos Barakitis, Stefano Serra Capizzano

Publikováno v: Numerical Linear Algebra with Applications. 28

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d8db124dc8fb0787c5b529becd8105b6
https://doi.org/10.1002/nla.2332

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Fast Total Variation Method Based on Iterative Reweighted Norm for Airborne Scanning Radar Super-Resolution Imaging

Autor: Jianyu Yang, Yin Zhang, Xingyu Tuo, Yulin Huang

Publikováno v: Remote Sensing
Volume 12
Issue 18
Remote Sensing, Vol 12, Iss 2877, p 2877 (2020)

The total variation (TV) method has been applied to realizing airborne scanning radar super-resolution imaging while maintaining the outline of the target. The iterative reweighted norm (IRN) approach is an algorithm for addressing the minimum Lp nor

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A Unified Scalable Equivalent Formulation for Schatten Quasi-Norms

Autor: Fanhua Shang, Yuanyuan Liu, Fanjie Shang, Hongying Liu, Lin Kong, Licheng Jiao

Publikováno v: Mathematics, Vol 8, Iss 1325, p 1325 (2020)
Mathematics
Volume 8
Issue 8

The Schatten quasi-norm is an approximation of the rank, which is tighter than the nuclear norm. However, most Schatten quasi-norm minimization (SQNM) algorithms suffer from high computational cost to compute the singular value decomposition (SVD) of

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https://www.mdpi.com/2227-7390/8/8/1325

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Computing functions of very large matrices with small TT/QTT ranks by quadrature formulas

Autor: Fabio Durastante, Daniele Bertaccini

Publikováno v: Journal of computational and applied mathematics 370 (2020). doi:10.1016/j.cam.2019.112663
info:cnr-pdr/source/autori:Bertaccini, D.; Durastante, F./titolo:Computing functions of very large matrices with small TT%2FQTT ranks by quadrature formulas/doi:10.1016%2Fj.cam.2019.112663/rivista:Journal of computational and applied mathematics/anno:2020/pagina_da:/pagina_a:/intervallo_pagine:/volume:370

The computation of matrix functions using quadrature formulas and rational approximations of very large structured matrices using tensor trains (TT), and quantized tensor trains (QTT) is considered here. The focus is on matrices with a small TT/QTT r

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a7559105d303ae3bd2975a912496437
http://hdl.handle.net/2108/230724

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