# Quantization-Based Optimization: Alternative Stochastic Approximation of Global Optimization

arXiv: https://arxiv.org/abs/2211.03972 · HTML: https://mlsanity.com/paper/2211.03972/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 48 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 3 of 3)

- [Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss](https://mlsanity.com/paper/1808.05779/) (2018)
- [Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms](https://mlsanity.com/paper/1506.06438/) (2015)
- [Quantum computing for energy systems optimization: Challenges and opportunities](https://mlsanity.com/paper/2003.00254/) (2020)

## Built on by (most cited first, 1 of 1)

- [Quantization-based Optimization with Perspective of Quantum Mechanics](https://mlsanity.com/paper/2308.11594/) (2023)

## Abstract (arXiv)

In this study, we propose a global optimization algorithm based on quantizing the energy level of an objective function in an NP-hard problem. According to the white noise hypothesis for a quantization error with a dense and uniform distribution, we can regard the quantization error as i.i.d. white noise. From stochastic analysis, the proposed algorithm converges weakly only under conditions satisfying Lipschitz continuity, instead of local convergence properties such as the Hessian constraint of the objective function. This shows that the proposed algorithm ensures global optimization by Laplace's condition. Numerical experiments show that the proposed algorithm outperforms conventional learning methods in solving NP-hard optimization problems such as the traveling salesman problem.