Earticle

초록

영어

Iterative gradient descent algorithms to solve algebraic linear equations are well known in the literature. These algorithms can be interpreted as dynamical closed loop control systems, where the step sizes are the control variables that can be optimally calculated by the Liapunov Control Functions (CLF) and Liapunov Optimizing Control (LOC) methods. In this paper matrix versions of gradient descent algorithms are deduced, including an unpublished matrix form of the Barzilai-Borwein algorithm. The step sizes (control variables) are also calculated by CLF/LOC. The main utility of these matrix algorithms is in image deblurring in the cases where the matrix that represents the blurring process is too large to be stored in the memory of the majority of conventional computational systems.

목차

Abstract
 1. Introduction
 2. Gradient Descent Algorithms Interpreted as Dynamical Closed Loop Control Systems
 3. Separable PSF and Matrix Forms
 4. Matrix Forms of Iterative Algorithms
  4.1. Steepest Descent, Orthomin and Barzilai-Borwein
  4.2. Conjugate Gradient
 5. Computational Experiments
 6. Conclusions
 References

키워드

저자

• Fernando Pazos [ Department of Electronics and Telecommunication Engineering, State University of Rio de Janeiro, Rua São Fransciso Xavier n° 524, Rio de Janeiro 20550-900, Brazil ]
• Amit Bhaya [ Department of Electrical Engineering, FederalUniversity of Rio de Janeiro, PEE/COPPE/UFRJ, POBox 68504, Rio de Janeiro 21945-970, Brazil ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605