LRINorm - A MATLAB Package for Rank Constrained Optimization by Low-Rank Inducing Norms and Non-Convex Proximal Splitting Methods. Low-rank rank inducing norms and non-convex Proximal Splitting Algoriths attempt to find exact rank/cardinality-r solutions to minimization problems with convex loss functions, i.e., avoiding of regularzation heuristics. This project provides MATLAB implementations for the proximal mappings of the low-rank inducing Frobenius and Spectral norms, as well as, their epi-graph projections and non-convex counter parts.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Grussler, Christian; Giselsson, Pontus: Efficient proximal mapping computation for low-rank inducing norms (2022)
- Grussler, Christian; Giselsson, Pontus: Low-rank inducing norms with optimality interpretations (2018)