Solving maximum cut problems by simulated annealing
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Abstract (arXiv)
This paper gives a straightforward implementation of simulated annealing for solving maximum cut problems and compares its performance to that of some existing heuristic solvers. The formulation used is classical, dating to a 1989 paper of Johnson, Aragon, McGeoch, and Schevon. This implementation uses no structure peculiar to the maximum cut problem, but its low per-iteration cost allows it to find better solutions than were previously known for 40 of the 89 standard maximum cut instances tested within a few minutes of computation.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 May 2015.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 2.
4 papers.
- 2025HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challengescited by 21
- 2023Local Energy Distribution Based Hyperparameter Determination for Stochastic Simulated Annealingcited by 2
- 2026GPU-accelerated simulated annealing based on p-bits with real-world device-variability modelingcited by 0
- 2026Enhanced Convergence in p-bit Based Simulated Annealing with Partial Deactivation for Large-Scale Combinatorial Optimization Problemscited by 0
- 2026Enhanced Convergence in p-bit Based Simulated Annealing with Partial Deactivation for Large-Scale Combinatorial Optimization Problems22 Jan 2026
- 2026GPU-accelerated simulated annealing based on p-bits with real-world device-variability modeling20 Jan 2026
- 2025HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges18 Jun 2025
- 2023Local Energy Distribution Based Hyperparameter Determination for Stochastic Simulated Annealing24 Apr 2023