Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/98555
| Title: | Computation of second-order directional stationary points for group sparse optimization | Authors: | Peng, D Chen, X |
Issue Date: | 2020 | Source: | Optimization methods and software, 2020, v. 35, no. 2, p. 348-376 | Abstract: | We consider a nonconvex and nonsmooth group sparse optimization problem where the penalty function is the sum of compositions of a folded concave function and the l2 vector norm for each group variable. We show that under some mild conditions a first-order directional stationary point is a strict local minimizer that fulfils the first-order growth condition, and a second-order directional stationary point is a strong local minimizer that fulfils the second-order growth condition. In order to compute second-order directional stationary points, we construct a twice continuously differentiable smoothing problem and show that any accumulation point of the sequence of second-order stationary points of the smoothing problem is a second-order directional stationary point of the original problem. We give numerical examples to illustrate how to compute a second-order directional stationary point by the smoothing method. | Keywords: | Group sparse optimization Nonconvex and nonsmooth optimization Composite folded concave penalty Directional stationary point Smoothing method |
Publisher: | Taylor & Francis | Journal: | Optimization methods and software | ISSN: | 1055-6788 | EISSN: | 1029-4937 | DOI: | 10.1080/10556788.2019.1684492 | Rights: | © 2019 Informa UK Limited, trading as Taylor & Francis Group This is an Accepted Manuscript of an article published by Taylor & Francis in Optimization Methods and Software on 04 Nov 2019 (published online), available at: http://www.tandfonline.com/10.1080/10556788.2019.1684492. |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Chen_Computation_Second-Order_Directional.pdf | Pre-Published version | 1 MB | Adobe PDF | View/Open |
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