LMaFit

LMaFit is a MATLAB package that currently solves the following problems: Matrix Complete (MC), Sparse Matrix Separation (SMS), Matrix Compressive Sensing (MCS).

Keywords for this software

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References in zbMATH (referenced in 49 articles )

Showing results 1 to 20 of 49.
Sorted by year (citations)

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  1. Driggs, Derek; Becker, Stephen; Aravkin, Aleksandr: Adapting regularized low-rank models for parallel architectures (2019)
  2. Jiang, Bo; Lin, Tianyi; Ma, Shiqian; Zhang, Shuzhong: Structured nonconvex and nonsmooth optimization: algorithms and iteration complexity analysis (2019)
  3. Wang, Kai; Desai, Jitamitra: On the convergence rate of the augmented Lagrangian-based parallel splitting method (2019)
  4. Wang, Yu; Yin, Wotao; Zeng, Jinshan: Global convergence of ADMM in nonconvex nonsmooth optimization (2019)
  5. Hao, Ruru; Su, Zhixun: A patch-based low-rank tensor approximation model for multiframe image denoising (2018)
  6. Yang, Lei; Pong, Ting Kei; Chen, Xiaojun: A nonmonotone alternating updating method for a class of matrix factorization problems (2018)
  7. Bouwmans, Thierry; Sobral, Andrews; Javed, Sajid; Jung, Soon Ki; Zahzah, El-Hadi: Decomposition into low-rank plus additive matrices for background/foreground separation: a review for a comparative evaluation with a large-scale dataset (2017)
  8. Chow, Yat Tin; Darbon, Jérôme; Osher, Stanley; Yin, Wotao: Algorithm for overcoming the curse of dimensionality for time-dependent non-convex Hamilton-Jacobi equations arising from optimal control and differential games problems (2017)
  9. Lee, Byungjoon; Darbon, Jérôme; Osher, Stanley; Kang, Myungjoo: Revisiting the redistancing problem using the Hopf-Lax formula (2017)
  10. Han, Le; Zhang, Qin: Multi-stage convex relaxation method for low-rank and sparse matrix separation problem (2016)
  11. Jia, Kui; Chan, Tsung-Han; Zeng, Zinan; Gao, Shenghua; Wang, Gang; Zhang, Tianzhu; Ma, Yi: ROML: a robust feature correspondence approach for matching objects in a set of images (2016)
  12. Jin, Zheng-Fen; Wan, Zhongping; Jiao, Yuling; Lu, Xiliang: An alternating direction method with continuation for nonconvex low rank minimization (2016)
  13. Ma, ShiQian; Yang, JunFeng: Applications of gauge duality in robust principal component analysis and semidefinite programming (2016)
  14. Tanner, Jared; Wei, Ke: Low rank matrix completion by alternating steepest descent methods (2016)
  15. Wei, Ke; Cai, Jian-Feng; Chan, Tony F.; Leung, Shingyu: Guarantees of Riemannian optimization for low rank matrix recovery (2016)
  16. Gao, Wei; Zhu, Linli; Wang, Kaiyun: Ontology sparse vector learning algorithm for ontology similarity measuring and ontology mapping via ADAL technology (2015)
  17. Hintermüller, Michael; Wu, Tao: Robust principal component pursuit via inexact alternating minimization on matrix manifolds (2015)
  18. Li, Yusheng; Xie, Xinchang; Yang, Zhouwang: Alternating direction method of multipliers for solving dictionary learning models (2015)
  19. Shang, Fanhua; Liu, Yuanyuan; Tong, Hanghang; Cheng, James; Cheng, Hong: Robust bilinear factorization with missing and grossly corrupted observations (2015)
  20. Wu, Tong Tong; Lange, Kenneth: Matrix completion discriminant analysis (2015)

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