# Convergence rate of a simulated annealing algorithm with noisy observations

arXiv: https://arxiv.org/abs/1703.00329 · HTML: https://mlsanity.com/paper/1703.00329/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2017.

## Built on by (most cited first, 3 of 3)

- [Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks](https://mlsanity.com/paper/2502.04850/) (2025)
- [Uniform Generalization Bound on Time and Inverse Temperature for Gradient Descent Algorithm and its Application to Analysis of Simulated Annealing](https://mlsanity.com/paper/2205.12959/) (2022)
- [An Invariant Information Geometric Method for High-Dimensional Online Optimization](https://mlsanity.com/paper/2401.01579/) (2024)

## Abstract (arXiv)

In this paper we propose a modified version of the simulated annealing algorithm for solving a stochastic global optimization problem. More precisely, we address the problem of finding a global minimizer of a function with noisy evaluations. We provide a rate of convergence and its optimized parametrization to ensure a minimal number of evaluations for a given accuracy and a confidence level close to 1. This work is completed with a set of numerical experimentations and assesses the practical performance both on benchmark test cases and on real world examples.