simulated annealing
Type: Concept
As of November 2026, simulated annealing has been mentioned in one podcast episode on Kaggle (Jan 2026). An optimization algorithm initially considered but later abandoned in favor of a better solution.
Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. For large numbers of local optima, SA can find the global optimum. It is often used when the search space is discrete. For problems where a fixed amount of computing resource is available, finding an approximate global optimum may be more relevant than attempting
| Mentioned in | 1 episode on 1 podcast (as of November 2026) |
|---|---|
| First heard | Jan 2026 on Kaggle |
| Field | Optimization |
Who talks about simulated annealing on podcasts?
simulated annealing came up; the most recent mention was Jan 2026 on Kaggle.
What is simulated annealing mentioned alongside?
simulated annealing is most often mentioned alongside alphabetical order, local optimization and B ranks.
Mentioned together: alphabetical order, local optimization, B ranks, function words, beam search, content words