Reduced-complexity Krylov subspace methods for large-scale MIMO channel estimation

2018 ◽  
Vol 78 ◽  
pp. 332-337 ◽  
Author(s):  
Jiayi Yang ◽  
Jun Tong ◽  
Qinghua Guo ◽  
Jiangtao Xi ◽  
Yanguang Yu
Keyword(s):  
Channel Estimation ◽  
Large Scale ◽  
Krylov Subspace ◽  
Krylov Subspace Methods ◽  
Mimo Channel ◽  
Subspace Methods ◽  
Reduced Complexity

POWER GRIDS ANALYSIS IN COMPRESSED KRYLOV-SUBSPACE METHODS

2008 ◽  
Vol 17 (03) ◽  
pp. 439-446
Author(s):  
HAOHANG SU ◽  
YIMEN ZHANG ◽  
YUMING ZHANG ◽  
JINCAI MAN
Keyword(s):  
Present Method ◽  
Large Scale ◽  
Krylov Subspace ◽  
Power Grid ◽  
Excellent Result ◽  
Krylov Subspace Methods ◽  
Power Grids ◽  
Improved Method ◽  
Subspace Methods ◽  
Transient Simulations

An improved method is proposed based on compressed and Krylov-subspace iterative approaches to perform efficient static and transient simulations for large-scale power grid circuits. It is implemented with CG and BiCGStab algorithms and an excellent result has been obtained. Extensive experimental results on large-scale power grid circuits show that the present method is over 200 times faster than SPICE3 and around 10–20 times faster than ICCG method in transient simulations. Furthermore, the presented algorithm saves the memory usage over 95% of SPICE3 and 75% of ICCG method, respectively while the accuracy is not compromised.

Global extended Krylov subspace methods for large-scale differential Sylvester matrix equations

2019 ◽  
Vol 62 (1-2) ◽  
pp. 157-177
Author(s):  
El. Mostafa Sadek ◽  
Abdeslem Hafid Bentbib ◽  
Lakhlifa Sadek ◽  
Hamad Talibi Alaoui
Keyword(s):  
Large Scale ◽  
Krylov Subspace ◽  
Krylov Subspace Methods ◽  
Matrix Equations ◽  
Subspace Methods ◽  
Sylvester Matrix ◽  
Sylvester Matrix Equations

The Minimum Norm Least-Squares Solution in Reduction by Krylov Subspace Methods

2016 ◽  
Vol 26 (01) ◽  
pp. 1750006 ◽  
Author(s):  
Xinsheng Wang ◽  
Chenxu Wang ◽  
Mingyan Yu
Keyword(s):  
Least Squares ◽  
Computer Aided Design ◽  
Large Scale ◽  
Krylov Subspace ◽  
Krylov Subspace Methods ◽  
Small Scale ◽  
Minimum Norm ◽  
Subspace Projection ◽  
Subspace Methods ◽  
Least Squares Solution

In recent years, model order reduction (MOR) of interconnect system has become an important technique to reduce the computation complexity and improve the verification efficiency in the nanometer VLSI design. The Krylov subspaces techniques in existing MOR methods are efficient, and have become the methods of choice for generating small-scale macro-models of the large-scale multi-port RCL networks that arise in VLSI interconnect analysis. Although the Krylov subspace projection-based MOR methods have been widely studied over the past decade in the electrical computer-aided design community, all of them do not provide a best optimal solution in a given order. In this paper, a minimum norm least-squares solution for MOR by Krylov subspace methods is proposed. The method is based on generalized inverse (or pseudo-inverse) theory. This enables a new criterion for MOR-based Krylov subspace projection methods. Two numerical examples are used to test the PRIMA method based on the method proposed in this paper as a standard model.

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