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A projected gradient method for nonlinear inverse problems with 𝛼ℓ1 − 𝛽ℓ2 sparsity regularization

  • Zhuguang Zhao and Liang Ding EMAIL logo
Published/Copyright: July 25, 2023

Abstract

The non-convex α 1 β 2 ( α β 0 ) regularization is a new approach for sparse recovery. A minimizer of the α 1 β 2 regularized function can be computed by applying the ST-( α 1 β 2 ) algorithm which is similar to the classical iterative soft thresholding algorithm (ISTA). Unfortunately, It is known that ISTA converges quite slowly, and a faster alternative to ISTA is the projected gradient (PG) method. Nevertheless, the current applicability of the PG method is limited to linear inverse problems. In this paper, we extend the PG method based on a surrogate function approach to nonlinear inverse problems with the α 1 β 2 ( α β 0 ) regularization in the finite-dimensional space R n . It is shown that the presented algorithm converges subsequentially to a stationary point of a constrained Tikhonov-type functional for sparsity regularization. Numerical experiments are given in the context of a nonlinear compressive sensing problem to illustrate the efficiency of the proposed approach.

MSC 2010: 49M37; 65K05

Award Identifier / Grant number: 2572021DJ03

Award Identifier / Grant number: LBH-Q16008

Award Identifier / Grant number: 41304093

Funding statement: The work of the second author was supported by the Fundamental Research Funds for the Central Universities (no. 2572021DJ03), Heilongjiang Postdoctoral Research Developmental Fund (no. LBH-Q16008) and the National Natural Science Foundation of China (no. 41304093).

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Received: 2023-01-27
Revised: 2023-05-14
Accepted: 2023-06-14
Published Online: 2023-07-25
Published in Print: 2024-06-01

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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