LRIPy - A Python Package for Rank Constrained Optimization by Low-Rank Inducing Norms and Non-Convex Proximal Splitting Methods. Python code for Low-rank optimization by Low-Rank Inducing Norms as well as non-convex Douglas-Rachford. Purpose: 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. LRIPy provides Python 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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