The Proximal Operator of the Piece-wise Exponential Function and Its Application in Compressed Sensing
Abstract
This paper characterizes the proximal operator of the piece-wise exponential function with a given shape parameter , which is a popular nonconvex surrogate of -norm in support vector machines, zero-one programming problems, and compressed sensing, etc. Although Malek-Mohammadi et al. [IEEE Transactions on Signal Processing, 64(21):5657--5671, 2016] once worked on this problem, the expressions they derived were regrettably inaccurate. In a sense, it was lacking a case. Using the Lambert W function and an extensive study of the piece-wise exponential function, we have rectified the formulation of the proximal operator of the piece-wise exponential function in light of their work. We have also undertaken a thorough analysis of this operator. Finally, as an application in compressed sensing, an iterative shrinkage and thresholding algorithm (ISTA) for the piece-wise exponential function regularization problem is developed and fully investigated. A comparative study of ISTA with nine popular non-convex penalties in compressed sensing demonstrates the advantage of the piece-wise exponential penalty.
Cite
@article{arxiv.2306.13425,
title = {The Proximal Operator of the Piece-wise Exponential Function and Its Application in Compressed Sensing},
author = {Yulan Liu and Yuyang Zhou and Rongrong Lin},
journal= {arXiv preprint arXiv:2306.13425},
year = {2023}
}