This article proposes a continuous-time optimization approch instead of tranditional optimiztion methods to address the nuclear norm minimization (NNM) problem. Refomulating the NNM into a matrix form, we propose a Lagrangian programming neural network (LPNN) to solve the NNM. Moreover, the convergence condtions of LPNN are presented by the Lyapunov method. Convergence experiments are presented to demonstrate the convergence of LPNN. Compared with tranditional algorithms of NNM, the proposed algorithm outperforms in terms of image recovery.
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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10852323 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0292380 | PLOS |
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