Zobrazeno 1 - 10
of 127
pro vyhledávání: '"YUJI NAKATSUKASA"'
Autor:
YUJI NAKATSUKASA1 nakatsukasa@maths.oxford.ac.uk, TROPP, JOEL A.2 jtropp@caltech.edu
Publikováno v:
SIAM Journal on Matrix Analysis & Applications. 2024, Vol. 45 Issue 2, p1183-1214. 32p.
Autor:
MEIER, MAIKE1 meier@maths.ox.ac.uk, YUJI NAKATSUKASA1 nakatsukasa@maths.ox.ac.uk, TOWNSEND, ALEX2 townsend@cornell.edu, WEBB, MARCUS3 marcus.webb@manchester.ac.uk
Publikováno v:
SIAM Journal on Matrix Analysis & Applications. 2024, Vol. 45 Issue 2, p905-929. 25p.
Publikováno v:
SIAM Journal on Scientific Computing; 2024, Vol. 46 Issue 2, pA929-A952, 24p
Publikováno v:
SIAM Journal on Matrix Analysis & Applications; 2024, Vol. 45 Issue 1, p619-633, 15p
Autor:
BENNER, PETER1 benner@mpi-magdeburg.mpg.de, YUJI NAKATSUKASA2 nakatsukasa@maths.ox.ac.uk, PENKE, CAROLIN1 penke@mpi-magdeburg.mpg.de
Publikováno v:
SIAM Journal on Matrix Analysis & Applications. 2023, Vol. 44 Issue 3, p1245-1270. 26p.
Autor:
YUJI NAKATSUKASA1 nakatsukasa@maths.ox.ac.uk, TAEJUN PARK1 park@maths.ox.ac.uk
Publikováno v:
SIAM Journal on Matrix Analysis & Applications. 2023, Vol. 44 Issue 3, p1370-1392. 23p.
Publikováno v:
Mathematical Programming. 193:447-483
We present an algorithm for the minimization of a nonconvex quadratic function subject to linear inequality constraints and a two-sided bound on the 2-norm of its solution. The algorithm minimizes the objective using an active-set method by solving a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d590af5026ea07610620513bc1eab856
https://doi.org/10.1007/s10107-021-01617-2
https://doi.org/10.1007/s10107-021-01617-2
Autor:
Yuji Nakatsukasa, Alex Townsend
Publikováno v:
SIAM Journal on Numerical Analysis. 59:314-333
An important observation in compressed sensing is that the $\ell_0$ minimizer of an underdetermined linear system is equal to the $\ell_1$ minimizer when there exists a sparse solution vector and a certain restricted isometry property holds. Here, we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39b551c0e0392f001063cab884982169
https://doi.org/10.1137/19m1242860
https://doi.org/10.1137/19m1242860
Publikováno v:
Linear Algebra and its Applications. 594:177-192
When a projection of a symmetric or Hermitian matrix to the positive semidefinite cone is computed approximately (or to working precision on a computer), a natural question is to quantify its accuracy. A straightforward bound invoking standard eigenv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9897b5385ff673a96b2de7d1db1d2620
https://doi.org/10.1016/j.laa.2020.02.014
https://doi.org/10.1016/j.laa.2020.02.014
Quantum subspace diagonalization methods are an exciting new class of algorithms for solving large\rev{-}scale eigenvalue problems using quantum computers. Unfortunately, these methods require the solution of an ill-conditioned generalized eigenvalue
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf426417380c042628d227f55d8f6c16
http://arxiv.org/abs/2110.07492
http://arxiv.org/abs/2110.07492
