Correction to: An Approximate Augmented Lagrangian Method for Nonnegative Low-Rank Matrix Approximation
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computational Theory and Mathematics,General Engineering,Theoretical Computer Science,Software,Applied Mathematics,Computational Mathematics,Numerical Analysis
Link
https://link.springer.com/content/pdf/10.1007/s10915-021-01729-z.pdf
Reference4 articles.
1. Attouch, H., Bolte, J., Redont, P., Soubeyran, A.: Proximal alternating minimization and projection methods for nonconvex problems: an approach based on the Kurdyka-łojasiewicz inequality. Math. Oper. Res. 35(2), 438–457 (2010)
2. Cason, T., Absil, P., Van Dooren, P.: Iterative methods for low rank approximation of graph similarity matrices. Linear Algebra Appl. 438(4), 1863–1882 (2013)
3. Schneider, R., Uschmajew, A.: Convergence results for projected line-search methods on varieties of low-rank matrices via lojasiewicz inequality. SIAM J. Optim. 25(1), 622–646 (2015)
4. Zhu, H., Ng, M., Song, G.: Augmented Lagrangian methods for nonnegative low-rank matrix approximation. J. Sci. Comput. 88 (2021)
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