# A Granular Sieving Algorithm for Deterministic Global Optimization

arXiv: https://arxiv.org/abs/2107.06581 · HTML: https://mlsanity.com/paper/2107.06581/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 35 references that are in the map.
- First posted on arXiv 14 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [A Literature Survey of Benchmark Functions For Global Optimization Problems](https://mlsanity.com/paper/1308.4008/) (2013)

## Abstract (arXiv)

A gradient-free deterministic method is developed to solve global optimization problems for Lipschitz continuous functions defined in arbitrary path-wise connected compact sets in Euclidean spaces. The method can be regarded as granular sieving with synchronous analysis in both the domain and range of the objective function. With straightforward mathematical formulation applicable to both univariate and multivariate objective functions, the global minimum value and all the global minimizers are located through two decreasing sequences of compact sets in, respectively, the domain and range spaces. The algorithm is easy to implement with moderate computational cost. The method is tested against extensive benchmark functions in the literature. The experimental results show remarkable effectiveness and applicability of the algorithm.