# Fast Solving Complete 2000-Node Optimization Using Stochastic-Computing Simulated Annealing

arXiv: https://arxiv.org/abs/2603.20197 · HTML: https://mlsanity.com/paper/2603.20197/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2026.

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

- [Memory-Efficient FPGA Implementation of Stochastic Simulated Annealing](https://mlsanity.com/paper/2601.18007/) (2026)

## Abstract (arXiv)

In this paper, we evaluate stochastic-computing simulated annealing (SC-SA) for solving large-scale combinatorial optimization problems. SC-SA is designed using stochastic computing, where the computatoin is reazlied using random bitstream, resulting in fast converging to the global minimum energy of the problems. The proposed SC-SA is compared with a typical SA and existing simulated-annealing (SA) processors on the maximum cut (MAX-CUT) problems, such as Gset that is a benchmark for SA. The simulation results show that SC-SA realizes a few orders of magnitude faster than a typical SA. In addition, SC-SA achieves better MAX-CUT scores than other existing methods on K2000 that is a complete 2000-node optimization problem.