ReLU-NMD is a relatively new nonlinear matrix decomposition model which, given a sparse non-negative matrix $X$, aims at finding a low-rank approximation $\Theta$ such that $\max(0,\Theta) \approx X$. One can compute such a rank- $r$ decomposition of $X$ by solving the Latent-ReLU-NMD model

$$ \lVert Z - \Theta \rVert_F^2 \quad \text{such that} \quad \max(0,Z)=X \text{ and } \text{rank}(\Theta) \leq r,$$

or the Three-block-ReLU-NMD (3B-ReLU-NMD) formulation:

$$ \lVert Z - WH \rVert_F^2 \quad \text{such that} \quad \max(0,Z)=X.$$

ReLU-NMD finds application in entry-dependent matrix completion [1], in the recovery of Euclidean distance matrices [4], in the compression of sparse data [2], and in manifold learning [3]. This repository contains state-of-the-art algorithms for computing ReLU-NMDs and examples of test problems. It allows one to reproduce the results presented in our paper [4]; see https://arxiv.org/abs/2503.23832.

Here is the list of implemented algorithms:

1. Naive-NMD: alternate optimization scheme that computes at each iteration one global optima of the subproblem for $Z$ and $\Theta$ in the Latent-ReLU-NMD formulation [3],
2. Aggressive-NMD (A_NMD): adaptively extrapolated version of the Naive-NMD scheme [2],
3. Expectation-minimization NMD (EM-NMD): expectation-minimization framework applied to the Latent-ReLU-NMD problem [3],
4. Block coordinate descent NMD (BCD-NMD): block coordinate descent algorithm which computes one global optima for each subproblem of the 3B-ReLU-NMD formulation [2],
5. Extrapolade BCD-NMD (eBCD-NMD): new extrapolated and provably convergent variant of the BCD-NMD [4].
6. Three block NMD (3B-NMD): heuristic extrapolation technique applied to the BCD-NMD schem [2].

The numerical experiments folder contains codes to replicate the experiments contained in the paper [4]:

1. Figure5_1: Synthetic matrix completion example with ReLU sampling,
2. Figure5_2: Euclidean distance matrix completion example recovering distance matrix of points with different distribution in a three-dimensional space,
3. Figure5_3: example using the Threshold Similarity Matching (TSM) approach[3] to compute lower-dimensional embedding of text data,
4. Figure5_4: script to visually observe compressed image using eBCD-NMD algorithm and compare with the truncated singular value approximation of the same rank,
5. Table5_1: uses ReLU decomposition to compress sparse data and images.

Other folders description:

1. Datasets: contains the data sets needed to run the codes,
2. utils: contains the functions needed to run the main codes,
3. Rank_1_modified_codes_for_EDMC: codes that solves a rank-1 modified version of the Latent-ReLU-NMD and 3B-ReLU-NMD needed for the recovery of Euclidean distance matrices, more details in [4].

References.

[1] H. Liu, P. Wang, L. Huang, Q. Qu, and L. Balzano, “Symmetric matrix completion with ReLU sampling,” arXiv preprint arXiv:2406.05822, 2024.

[2] G. Seraghiti, A. Awari, A. Vandaele, M. Porcelli, and N. Gillis, “Accelerated algorithms for nonlinear matrix decom- position with the relu function,” in 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6, IEEE, 2023.

[3] L. K. Saul, “A geometrical connection between sparse and low-rank matrices and its application to manifold learning,” Transactions on Machine Learning Research, 2022.

[4] M. Porcelli, N. Gillis, and G. Seraghiti, "An extrapolated and provably convergent algorithm or nonlinear matrix decomposition with the ReLU function", https://arxiv.org/abs/2503.23832, 2025.