# Distributed Majorization-Minimization for Laplacian Regularized Problems

arXiv: https://arxiv.org/abs/1803.10317 · HTML: https://mlsanity.com/paper/1803.10317/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [PPFL: A Personalized Federated Learning Framework for Heterogeneous Population](https://mlsanity.com/paper/2310.14337/) (2023)
- [Learning to Optimize by Differentiable Programming](https://mlsanity.com/paper/2601.16510/) (2026)
- [Spectral Gap Regularization of Neural Networks](https://mlsanity.com/paper/2304.03096/) (2023)
- [Difference-of-Convex Regularization for Graph Learning by Differentiable Programming](https://mlsanity.com/paper/2608.12757/) (2026)

## Abstract (arXiv)

We consider the problem of minimizing a block separable convex function (possibly nondifferentiable, and including constraints) plus Laplacian regularization, a problem that arises in applications including model fitting, regularizing stratified models, and multi-period portfolio optimization. We develop a distributed majorization-minimization method for this general problem, and derive a complete, self-contained, general, and simple proof of convergence. Our method is able to scale to very large problems, and we illustrate our approach on two applications, demonstrating its scalability and accuracy.