Solvers

Algorithms which are called solvers are then used to solve the minimization problem in its original form or in its Lagrangian form. Furthermore, instead of directly solving the original convex optimizations, some authors use their strongly convex approximations in order to design efficient algorithms.

Practically, the solvers can be broadly classified into two categories as follows:

• Regularization based approaches

• Statistical inference based approaches

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As this website gives many information that come from my research, please cite my following survey papers:

T. Bouwmans, A. Sobral, S. Javed, S. Jung, E. Zahzah, "Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset", Computer Science Review, Volume 23, pages 1-71, February 2017. [pdf]

T. Bouwmans, E. Zahzah, “Robust PCA via Principal Component Pursuit: A Review for a Comparative Evaluation in Video Surveillance”, Special Issue on Background Models Challenge, Computer Vision and Image Understanding, CVIU 2014, Volume 122, pages 22–34, May 2014. [pdf]

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