Convergence analysis of the augmented Lagrange multiplier algorithm for a class of matrix compressive recovery
| Autor: | Jin Wang, Chuan-Long Wang, Chao Li |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Class (set theory)
Applied Mathematics 020206 networking & telecommunications Augmented lagrange multiplier 0102 computer and information sciences 02 engineering and technology Computer Science::Numerical Analysis 01 natural sciences Matrix (mathematics) Equivalent model 010201 computation theory & mathematics Convergence (routing) 0202 electrical engineering electronic engineering information engineering Algorithm Mathematics |
| Zdroj: | Applied Mathematics Letters. 59:12-17 |
| ISSN: | 0893-9659 |
| DOI: | 10.1016/j.aml.2016.02.022 |
| Popis: | In this paper, we mainly discuss the convergence of the augmented Lagrange multiplier (ALM) algorithm for matrix compressive recovery presented in Wright et al. (2013). Because of the unknown ∂ ‖ P Ω ( A ) ‖ ∗ , it is hard to obtain the convergence. So we convert the model in Wright et al. (2013) to the equivalent model in Meng et al. (2014), and discuss the convergence of the ALM algorithm for the model in Meng et al. (2014). Finally, the numerical experiments show the convergence of the ALM algorithm for the matrix compressive recovery. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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