# Iterative proportional scaling revisited: a modern optimization perspective

arXiv: https://arxiv.org/abs/1610.02588 · HTML: https://mlsanity.com/paper/1610.02588/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2016.

## Built on by (most cited first, 1 of 1)

- [Toric Geometry of Entropic Regularization](https://mlsanity.com/paper/2202.01571/) (2022)

## Abstract (arXiv)

This paper revisits the classic iterative proportional scaling (IPS) from a modern optimization perspective. In contrast to the criticisms made in the literature, we show that based on a coordinate descent characterization, IPS can be slightly modified to deliver coefficient estimates, and from a majorization-minimization standpoint, IPS can be extended to handle log-affine models with features not necessarily binary-valued or nonnegative. Furthermore, some state-of-the-art optimization techniques such as block-wise computation, randomization and momentum-based acceleration can be employed to provide more scalable IPS algorithms, as well as some regularized variants of IPS for concurrent feature selection.