An approximate augmented Lagrangian method for nonnegative low-rank matrix approximation

DOI10.1007/s10915-021-01556-2zbMath1476.90318OpenAlexW3177781141MaRDI QIDQ2051047

Guang-Jing Song, Hong Zhu, Michael Kwok-Po Ng

Publication date: 1 September 2021

Published in: Journal of Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10915-021-01556-2

zbMATH Keywords

augmented Lagrangian methodnonnegative matrix factorizationlow-rank matrixnonnegative low-rank matrix approximation

Mathematics Subject Classification ID

Nonlinear programming (90C30)

Related Items (2)

Low-rank nonnegative tensor approximation via alternating projections and sketching ⋮ Correction to: ``An approximate augmented Lagrangian method for nonnegative low-rank matrix approximation

Cites Work

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• Fast Nonnegative Matrix Factorization: An Active-Set-Like Method and Comparisons
• Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
• Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming
• Variational Analysis
• A New Active Set Method For Nonnegative Matrix Factorization
• Learning the parts of objects by non-negative matrix factorization

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