Full Space and Subspace Methods for Large Scale Image Restoration

2010 ◽  
pp. 183-201
Author(s):  
Yanfei Wang ◽  
Shiqian Ma ◽  
Qinghua Ma
Keyword(s):  
Image Restoration ◽  
Large Scale ◽  
Subspace Methods

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.

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

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

scholarly journals A conjugate gradient algorithm and its application in large-scale optimization problems and image restoration

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Gonglin Yuan ◽  
Tingting Li ◽  
Wujie Hu
Keyword(s):  
Image Restoration ◽  
Conjugate Gradient ◽  
Large Scale ◽  
Optimization Problems ◽  
Trust Region ◽  
Conjugate Gradient Algorithm ◽  
Gradient Algorithm ◽  
Smooth Functions ◽  
Nonsmooth Functions ◽  
Large Scale Unconstrained Optimization

Abstract To solve large-scale unconstrained optimization problems, a modified PRP conjugate gradient algorithm is proposed and is found to be interesting because it combines the steepest descent algorithm with the conjugate gradient method and successfully fully utilizes their excellent properties. For smooth functions, the objective algorithm sufficiently utilizes information about the gradient function and the previous direction to determine the next search direction. For nonsmooth functions, a Moreau–Yosida regularization is introduced into the proposed algorithm, which simplifies the process in addressing complex problems. The proposed algorithm has the following characteristics: (i) a sufficient descent feature as well as a trust region trait; (ii) the ability to achieve global convergence; (iii) numerical results for large-scale smooth/nonsmooth functions prove that the proposed algorithm is outstanding compared to other similar optimization methods; (iv) image restoration problems are done to turn out that the given algorithm is successful.

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