The Proximal Operator of the Piece-wise Exponential Function and Its Application in Compressed Sensing

TL;DR

This paper accurately characterizes the proximal operator of the piece-wise exponential function, corrects previous inaccuracies, and applies it to develop an ISTA algorithm that outperforms other non-convex penalties in compressed sensing.

Contribution

It provides a corrected and comprehensive formulation of the proximal operator for the piece-wise exponential function and demonstrates its effectiveness in compressed sensing applications.

Findings

The proximal operator is explicitly derived using Lambert W function.

The ISTA algorithm with this penalty outperforms nine other non-convex penalties.

The piece-wise exponential penalty shows advantages in compressed sensing tasks.

Abstract

This paper characterizes the proximal operator of the piece-wise exponential function $1\!-\!e^{-|x|/\sigma}$ with a given shape parameter $\sigma\!>\!0$, which is a popular nonconvex surrogate of $\ell_0$-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.