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arXiv Machine Learning By Flavia Esposito, Andersen Ang

Optimization on the Oblique Manifold for Sparse Simplex Constraints via Multiplicative Updates

Read the original on arXiv Machine Learning →

arXiv:2503. 24075v4 Announce Type: replace-cross Abstract: Low-rank optimization problems with sparse simplex constraints involve variables that must satisfy nonnegativity, sparsity, and sum-to-1 conditions, making their optimization particularly challenging due to the interplay between low-rank structures and constraints.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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