Results 221 to 230 of about 190,411 (265)
Some of the next articles are maybe not open access.
Numerical stabilization of polynomial and matrix
IMA Journal of Mathematical Control and Information, 2006In this paper, the conception of numerical stabilization, which is related to mantissa digits of computer and dimensions of system, is described; and several strategies for the numerical stabilization of polynomial and matrix are presented.
J. D. Han, Z. Jiang, Yiyong Nie
openaire +1 more source
Superoscillations with Optimal Numerical Stability
IEEE Signal Processing Letters, 2014A bandlimited signal can oscillate at a rate faster than its bandlimit. This phenomenon, called "superoscillation", has ap- plications e.g. in superresolution and superdirectivity. The syn- thesis of superoscillations is a numerically difficult problem.
Lee, Dae Gwan +1 more
openaire +2 more sources
On the numerical stability of algorithmic differentiation
Computing, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Andreas Griewank +2 more
openaire +2 more sources
Numerical Stability of the Parallel Jacobi Method
SIAM Journal on Matrix Analysis and Applications, 2005The authors analyse the numerical stability of the parallel Jacobi method for computing the singular values and singular subspaces of an invertible upper triangular matrix obtained from QR decomposition with column pivoting. They show that in this case the parallel Jacobi method works with full machine accuracy, thus the computational errors are ...
Tristan Londré, Noah H. Rhee
openaire +2 more sources
Numerical Stabilization of Orbital Motion
Celestial Mechanics and Dynamical Astronomy, 2003Mainly, the author focuses on Baumgarte's method and its applications in satellite, asteroid, stellar and planetary problems. In the paper arguments are given for the use of energy relations for stabilization in the elliptical two-body problem. Stabilizing properties of Baumgarte's equations and others are discussed.
openaire +1 more source
BIT Numerical Mathematics, 1995
The generalized minimal residual (GMRES) method is one of the most popular methods for solving systems of linear equations with nonsymmetric coefficient matrices. The authors study the numerical stability of GMRES when the computation of approximations is based on constructing an orthonormal basis of Krylov subspaces (Arnoldi basis) and after that the ...
Drkošová, J. +3 more
openaire +1 more source
The generalized minimal residual (GMRES) method is one of the most popular methods for solving systems of linear equations with nonsymmetric coefficient matrices. The authors study the numerical stability of GMRES when the computation of approximations is based on constructing an orthonormal basis of Krylov subspaces (Arnoldi basis) and after that the ...
Drkošová, J. +3 more
openaire +1 more source
Stability and conditioning in numerical analysis
2006Summary: The terms stability and conditioning are used with a variety of meanings in numerical analysis. All of them have in common the general concept of the response of a computational algorithm to perturbations arising either from the data or from the specific arithmetic used on computers.
IAVERNARO, Felice +2 more
openaire +2 more sources
Stability of the Numerical Method
2016Stability in numerical analysis means that the approximate solutions admit the same bounds as indicated by the a priori estimates for the original problem. The fact that the numerical solutions satisfy the energy inequality ( 7.28) plays a crucial role.
Eduard Feireisl +2 more
openaire +1 more source
Stability of a Numerical Solution of Differential Equations
Journal of the ACM, 1959In 1926 Milne [1] published a numerical method for the solution of ordinary differential equations. This method turns out to be unstable, as shown by Muhin [2], Hildebrand [3], Liniger [4], and others. Instability was not too serious in the day of desk calculators but is fatal in the modern era of high speed computers.
W. E. Milne, R. R. Reynolds
openaire +1 more source
Numerical Stability Properties
2008Designers of algorithms must not only solve the problem of interest, but do so using methods which are robust under perturbations in the data as well as the intermediate parameters of the method. More generally, it is often the case that the actual problem of interest is too complicated to solve directly; simplifying assumptions are necessary.
Phillip Regalia, Richard Le Borne
openaire +1 more source