# The proximal point method revisited

arXiv: https://arxiv.org/abs/1712.06038 · HTML: https://mlsanity.com/paper/1712.06038/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2017.

## Built on by (most cited first, 9 of 9)

- [Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction](https://mlsanity.com/paper/2403.17042/) (2024)
- [SAM as an Optimal Relaxation of Bayes](https://mlsanity.com/paper/2210.01620/) (2022)
- [Stochastic Bias-Reduced Gradient Methods](https://mlsanity.com/paper/2106.09481/) (2021)
- [Rewind-to-Delete: Certified Machine Unlearning for Nonconvex Functions](https://mlsanity.com/paper/2409.09778/) (2024)
- [The Potential of Second-Order Optimization for LLMs: A Study with Full Gauss-Newton](https://mlsanity.com/paper/2510.09378/) (2025)
- [A Defense of the Quadratic Model](https://mlsanity.com/paper/2607.21716/) (2026)
- [Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning](https://mlsanity.com/paper/2402.09542/) (2024)
- [Active manifolds, stratifications, and convergence to local minima in nonsmooth optimization](https://mlsanity.com/paper/2108.11832/) (2021)
- [Target-based Surrogates for Stochastic Optimization](https://mlsanity.com/paper/2302.02607/) (2023)

## Abstract (arXiv)

In this short survey, I revisit the role of the proximal point method in large scale optimization. I focus on three recent examples: a proximally guided subgradient method for weakly convex stochastic approximation, the prox-linear algorithm for minimizing compositions of convex functions and smooth maps, and Catalyst generic acceleration for regularized Empirical Risk Minimization.