Fast RLS-type algorithm for unbiased equation-error adaptive IIR filtering based on approximate inverse-power iteration

2005 ◽  
Vol 53 (11) ◽  
pp. 4169-4185 ◽  
Author(s):  
Da-Zheng Feng ◽  
Wei Xing Zheng
Keyword(s):  
Inverse Power ◽  
Approximate Inverse ◽  
Power Iteration ◽  
Type Algorithm ◽  
Equation Error

Algebraic Connectivity Estimation Based on Decentralized Inverse Power Iteration

2016 ◽  
Vol 19 (2) ◽  
pp. 805-812
Author(s):  
Yue Wei ◽  
Hao Fang ◽  
Jie Chen ◽  
Bin Xin
Keyword(s):  
Inverse Power ◽  
Algebraic Connectivity ◽  
Power Iteration

On controllability of the real shifted inverse power iteration

2001 ◽  
Vol 43 (1) ◽  
pp. 9-23 ◽  
Author(s):  
Uwe Helmke ◽  
Fabian Wirth
Keyword(s):  
Inverse Power ◽  
Power Iteration ◽  
The Real

scholarly journals ADAPTIVE SOLUTION OF THE NEUTRON DIFFUSION EQUATION WITH HETEROGENEOUS COEFFICIENTS USING THE MIXED FINITE ELEMENT METHOD ON STRUCTURED MESHES

2021 ◽  
Vol 247 ◽  
pp. 02002
Author(s):  
Minh-Hieu Do ◽  
Patrick Ciarlet ◽  
François Madiot
Keyword(s):  
Finite Element ◽  
Eigenvalue Problem ◽  
Diffusion Equation ◽  
Neutron Transport ◽  
Mesh Refinement ◽  
Generalized Eigenvalue Problem ◽  
Inverse Power ◽  
Computational Time ◽  
Power Iteration ◽  
Generalized Eigenvalue

The neutron transport equation can be used to model the physics of the nuclear reactor core. Its solution depends on several variables and requires a lot of high precision computations. One can simplify this model to obtain the SPN equation for a generalized eigenvalue problem. In order to solve this eigenvalue problem, we usually use the inverse power iteration by solving a source problem at each iteration. Classically, this problem can be recast in a mixed variational form, and then discretized by using the Raviart-Thomas-Nédélec Finite Element. In this article, we focus on the steady-state diffusion equation with heterogeneous coefficients discretized on Cartesian meshes. In this situation, it is expected that the solution has low regularity. Therefore, it is necessary to refine at the singular regions to get better accuracy. The Adaptive Mesh Refinement (AMR) is one of the most effective ways to do that and to improve the computational time. The main ingredient for the refinement techniques is the use of a posteriori error estimates, which gives a rigorous upper bound of the error between the exact and numerical solution. This indicator allows to refine the mesh in the regions where the error is large. In this work, some mesh refinement strategies are proposed on the Cartesian mesh for the source problem. Moreover, we numerically investigate an algorithm which combines the AMR process with the inverse power iteration to handle the generalized eigenvalue problem.

Inverse Power Law Behavior in a Memory Scanning Experiment

2006 ◽  
Author(s):  
Gerardo Ramirez ◽  
Sonia Perez ◽  
John G. Holden
Keyword(s):  
Power Law ◽  
Inverse Power ◽  
Memory Scanning ◽  
Inverse Power Law
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