DeepAI AI Chat
Log In Sign Up

Variable metric extrapolation proximal iterative hard thresholding method for ℓ_0 minimization problem

08/07/2021
by   Xue Zhang, et al.
Shanghai Jiao Tong University
0

In this paper, we consider the ℓ_0 minimization problem whose objective function is the sum of ℓ_0-norm and convex differentiable function. A variable metric type method which combines the PIHT method and the skill in quasi-newton method, named variable metric extrapolation proximal iterative hard thresholding (VMEPIHT) method, is proposed. Then we analyze its convergence, linear convergence rate and superlinear convergence rate under appropriate assumptions. Finally, we conduct numerical experiments on compressive sensing problem and CT image reconstruction problem to confirm VMPIHT method's efficiency, compared with other state-of-art methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

11/01/2022

The standard forms of the Kaczmarz-Tanabe type methods and their convergence theory

In this paper, we consider the standard form of two kinds of Kaczmarz-Ta...
07/10/2019

A family of multi-parameterized proximal point algorithms

In this paper, a multi-parameterized proximal point algorithm combining ...
05/14/2020

Efficient iterative thresholding algorithms with functional feedbacks and convergence analysis

An accelerated class of adaptive scheme of iterative thresholding algori...
12/02/2011

Mask Iterative Hard Thresholding Algorithms for Sparse Image Reconstruction of Objects with Known Contour

We develop mask iterative hard thresholding algorithms (mask IHT and mas...