arXiv Machine Learning By Emanuele Zangrando, Sara Venturini, Francesco Rinaldi, Francesco Tudisco

Constrained Variable Projection for Structured Problems

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arXiv:2606. 23939v1 Announce Type: cross Abstract: Variable projection is a classical technique for separable nonlinear least-squares problems, in which variables that enter linearly are eliminated exactly, yielding a reduced nonlinear problem.

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arXiv Machine Learning
Jul 2

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

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.

By Flavia Esposito, Andersen Ang
arXiv Machine Learning
Jun 15

Neural Slack Variables for Shape Constraints

arXiv:2606. 13803v1 Announce Type: new Abstract: Enforcing functional inequality constraints such as monotonicity and convexity in neural networks is a fundamental challenge in many industrial and scientific applications.

By Ruben Wiedemann, Antoine Jacquier, Lukas Gonon