.png){kind=icon}
.png){kind=icon}
Return
CAPPA: Continuous-Time Accelerated Proximal Point Algorithm for Sparse Recovery
.png){kind=icon}
OA
AI
DOI：10.1109/LSP.2020.3027490.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This letter develops a novel Continuous-Time Accelerated Proximal Point Algorithm (CAPPA) for l(1)-minimization problems with provable fixed-time convergence guarantees. The problem of l(1)-minimization appears in several contexts such as Sparse Recovery (SR) in Compressed Sensing (CS) theory and sparse linear and logistic regressions in machine learning. Most existing algorithms for solving l(1)-minimization problems are discrete-time and require exhaustive computer-guided iterations. CAPPA alleviates this problem on two fronts: (a) it encompasses a continuous-time algorithm that can be implemented using analog circuits; (b) it outperforms Locally Competitive Algorithm (LCA) and finite-time LCA (recently developed continuous-time dynamical systems for solving SR problems) by exhibiting provable fixed-time convergence to optimal solution. Consequently, CAPPA is better suited for fast and efficient handling of SR problems.
Keywords:
Compressed sensing
Compressed sensing
Lyapunov methods
optimization methods
signal reconstruction
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W