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math.NA2026
The ReLU Matrix Decomposition: NP-Hardness, Ill-Posedness, and ReLU-Rank
Giovanni Seraghiti, Arnaud Vandaele, Nicolas Gillis
The ReLU Matrix Decomposition (RMD) approximates a sparse, nonnegative m-by-n matrix X by max(0, WH), where W is an m-by-r matrix, H is an r-by-n matrix, and r is the rank of the d…
math.OC2026
bAdag: an adaptive block coordinate gradient method for smooth nonconvex functions
Giovanni Seraghiti
A new Block Coordinate Gradient (BCG) method, dubbed bAdag, for smooth, nonconvex minimization problem is proposed; it falls in the class of Objective Function Free Optimization (O…
cs.LG2026
Nonnegative Matrix Factorization in the Component-Wise L1 Norm for Sparse Data
Giovanni Seraghiti, Kévin Dubrulle, Arnaud Vandaele +1
Nonnegative matrix factorization (NMF) approximates a nonnegative matrix, X, by the product of two nonnegative factors, WH, where W has r columns and H has r rows. In this paper, w…