Proportionate Adaptive Graph Signal Recovery

Torkamani, Razieh; Zayyani, Hadi; Korki, Mehdi

doi:10.1109/tsipn.2023.3277591

Cited by 8 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As mentioned in [7], for the saddle point problem, the optimistic gradient descent-ascent (OGDA) method updates the variable via the difference of the preceding two gradients. Besides, for the least mean squares (LMS) estimation of graph signals, the extended LMS algorithm in [8] and proportionate-type graph LMS algorithm in [9] update the variable via the weighted sum of the preceding several gradients. Inspired by the above works, we here propose an extended gradient method, in which the variables are updated along the direction of the sum of the gradients of the preceding two iterates.…”

Section: Introductionmentioning

confidence: 99%

An extended gradient method for smooth strongly convex functions

Zhang

Liu

Zhao

2023

Preprint

View full text Add to dashboard Cite

This paper considers an extended gradient method with fixed step size applied to the centralized and decentralized strongly convex and smooth functions. Firstly, the range of step size is derived to guarantee that the function value generated by the extended gradient method converges to the optimal solution at a sublinear rate. Then, we further show that the iterate sequences converge to the optimal solution at a linear rate for the centralized and decentralized problems. Finally, we demonstrate the efficiency of the proposed extended gradient method in numerical experiments.

show abstract

Section: Introductionmentioning

confidence: 99%

An extended gradient method for smooth strongly convex functions

Zhang

Liu

Zhao

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…As mentioned in [7,8], the optimistic gradient descent-ascent (OGDA) method and the inertial gradient algorithm with Hessian dampling (IGAHD) update the variable via the difference of the gradients of the preceding two iterates. Besides, for the least mean squares (LMS) estimation of graph signals, the extended LMS algorithm in [9] and proportionate-type graph LMS algorithm in [10] update the variable via the weighted sum of the gradients of the preceding several iterates. Further, the Anderson acceleration gradient descent method (AA-PGA) in [11] updates the variable by employing the convex combination of the gradients of the preceding several iterates.…”

Section: Introductionmentioning

confidence: 99%

An Extended Gradient Method for Smooth and Strongly Convex Functions

Zhang,

Liu,

Zhao

2023

Mathematics

View full text Add to dashboard Cite

In this work, we introduce an extended gradient method that employs the gradients of the preceding two iterates to construct the search direction for the purpose of solving the centralized and decentralized smooth and strongly convex functions. Additionally, we establish the linear convergence for iterate sequences in both the centralized and decentralized manners. Furthermore, the numerical experiments demonstrate that the centralized extended gradient method can achieve faster acceleration than the compared algorithms, and the search direction also exhibits the capability to improve the convergence of the existing algorithms in both two manners.

show abstract