{ "id": "2007.11377", "version": "v1", "published": "2020-07-22T12:40:09.000Z", "updated": "2020-07-22T12:40:09.000Z", "title": "$α\\ell_{1}-β\\ell_{2}$ sparsity regularization for nonlinear ill-posed problems", "authors": [ "Liang Ding", "Weimin Han" ], "comment": "33 pages, 4 figures", "categories": [ "math.NA", "cs.NA" ], "abstract": "In this paper, we consider the $\\alpha\\| \\cdot\\|_{\\ell_1}-\\beta\\| \\cdot\\|_{\\ell_2}$ sparsity regularization with parameter $\\alpha\\geq\\beta\\geq0$ for nonlinear ill-posed inverse problems. We investigate the well-posedness of the regularization. Compared to the case where $\\alpha>\\beta\\geq0$, the results for the case $\\alpha=\\beta\\geq0$ are weaker due to the lack of coercivity and Radon-Riesz property of the regularization term. Under certain condition on the nonlinearity of $F$, we prove that every minimizer of $ \\alpha\\| \\cdot\\|_{\\ell_1}-\\beta\\| \\cdot\\|_{\\ell_2}$ regularization is sparse. For the case $\\alpha>\\beta\\geq0$, if the exact solution is sparse, we derive convergence rate $O(\\delta^{\\frac{1}{2}})$ and $O(\\delta)$ of the regularized solution under two commonly adopted conditions on the nonlinearity of $F$, respectively. In particular, it is shown that the iterative soft thresholding algorithm can be utilized to solve the $ \\alpha\\| \\cdot\\|_{\\ell_1}-\\beta\\| \\cdot\\|_{\\ell_2}$ regularization problem for nonlinear ill-posed equations. Numerical results illustrate the efficiency of the proposed method.", "revisions": [ { "version": "v1", "updated": "2020-07-22T12:40:09.000Z" } ], "analyses": { "subjects": [ "65K10", "G.1.6" ], "keywords": [ "nonlinear ill-posed problems", "sparsity regularization", "nonlinear ill-posed inverse problems", "nonlinear ill-posed equations", "regularization problem" ], "note": { "typesetting": "TeX", "pages": 33, "language": "en", "license": "arXiv", "status": "editable" } } }