# Local Energy Distribution Based Hyperparameter Determination for Stochastic Simulated Annealing

arXiv: https://arxiv.org/abs/2304.11839 · HTML: https://mlsanity.com/paper/2304.11839/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 24 Apr 2023.

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

- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing](https://mlsanity.com/paper/1509.08972/) (2015)
- [Solving maximum cut problems by simulated annealing](https://mlsanity.com/paper/1505.03068/) (2015)

## Built on by (most cited first, 2 of 2)

- [GPU-accelerated simulated annealing based on p-bits with real-world device-variability modeling](https://mlsanity.com/paper/2601.14476/) (2026)
- [Enhanced Convergence in p-bit Based Simulated Annealing with Partial Deactivation for Large-Scale Combinatorial Optimization Problems](https://mlsanity.com/paper/2601.15561/) (2026)

## Abstract (arXiv)

This paper presents a local energy distribution based hyperparameter determination for stochastic simulated annealing (SSA). SSA is capable of solving combinatorial optimization problems faster than typical simulated annealing (SA), but requires a time-consuming hyperparameter search. The proposed method determines hyperparameters based on the local energy distributions of spins (probabilistic bits). The spin is a basic computing element of SSA and is graphically connected to other spins with its weights. The distribution of the local energy can be estimated based on the central limit theorem (CLT). The CLT-based normal distribution is used to determine the hyperparameters, which reduces the time complexity for hyperparameter search from O(n^3) of the conventional method to O(1). The performance of SSA with the determined hyperparameters is evaluated on the Gset and K2000 benchmarks for maximum-cut problems. The results show that the proposed method achieves mean cut values of approximately 98% of the best-known cut values.