# Distributed Global Optimization by Annealing

## TL;DR

This paper introduces a distributed optimization algorithm that uses annealing with Gaussian noise to find global minima of nonconvex functions, with practical verification methods and an application to target localization.

## Contribution

It proposes a novel distributed annealing algorithm for global minimization and provides practical verification techniques and an application example.

## Key findings

- Converges to global minima under certain conditions
- Applicable to distributed target localization
- Provides verification methods for assumptions

## Abstract

The paper considers a distributed algorithm for global minimization of a nonconvex function. The algorithm is a first-order consensus + innovations type algorithm that incorporates decaying additive Gaussian noise for annealing, converging to the set of global minima under certain technical assumptions. The paper presents simple methods for verifying that the required technical assumptions hold and illustrates it with a distributed target-localization application.