# A proximal point algorithm revisited and extended

## TL;DR

This paper revisits and extends a proximal point algorithm for finding zeros of maximal monotone operators, providing convergence analysis, practical implementation guidance, and simulation results to demonstrate its effectiveness.

## Contribution

It introduces a more general proximal point algorithm with convergence guarantees and practical considerations, extending previous methods in the field.

## Key findings

- The algorithm converges strongly under specified conditions.
- It can approximate minimizers of convex functionals.
- Simulations confirm practical applicability.

## Abstract

This Note is inspired by the recent paper by Djafary Rouhani and Moradi [J. Optim. Theory Appl. 172 (2017) 222-235], where a proximal point algorithm proposed by Boikanyo and Moro\c{s}anu [Optim. Lett. 7 (2013) 415-420] is discussed. We start with a brief history of the subject and then propose and analyse the following more general algorithm for approximating the zeroes of a maximal monotone operator $A$ in real Hilbert space $H$ $$ x_{n+1}=(I+\beta_nA)^{-1}(u_n + \alpha_n(x_n+e_n)), \ \ n\ge 0\, , $$ where $x_0\in H$ is a given starting point, $u_n \rightarrow u$ is a given sequence in $H$, ${R} \ni \alpha_n \rightarrow 0$, and $(e_n)$ is the error sequence satisfying $\alpha_ne_n\rightarrow 0$. Besides the main result on the strong convergence of $(x_n)$, we discuss some particular cases, including the approximation of minimizers of convex functionals, explain how to use our algorithm in practice, and present some simulations to illustrate the applicability of our algorithm.