# Accelerated Proximal Point Method for Maximally Monotone Operators

arXiv: https://arxiv.org/abs/1905.05149 · HTML: https://mlsanity.com/paper/1905.05149/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2019.

## Built on by (most cited first, 2 of 2)

- [Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions](https://mlsanity.com/paper/2203.09436/) (2022)
- [Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization](https://mlsanity.com/paper/2101.12101/) (2021)

## Abstract (arXiv)

This paper proposes an accelerated proximal point method for maximally monotone operators. The proof is computer-assisted via the performance estimation problem approach. The proximal point method includes various well-known convex optimization methods, such as the proximal method of multipliers and the alternating direction method of multipliers, and thus the proposed acceleration has wide applications. Numerical experiments are presented to demonstrate the accelerating behaviors.