How can simulated annealing be used to solve the problem?
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Simulated annealing can be used to solve various problems. It is particularly useful in index tracking (IT) for building passive portfolios that aim to follow a financial index while minimizing transaction costs . Simulated annealing is also applied in the Boltzmann machine for pattern recognition and classification problems . In the context of resource allocation for streaming or long-running workloads in the public cloud, simulated annealing helps navigate performance/cost trade-offs when selecting from heterogeneous service offerings . Additionally, simulated annealing is used in solving the student-project allocation problem, where it allows for the rapid determination of high-quality solutions by matching students to projects based on their preferences and supervisor workload constraints . Furthermore, simulated annealing is employed in solving the directed Steiner tree problem, where it provides better quality solutions compared to existing approaches .
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| Papers (4) | Insight |
|---|---|
| Simulated annealing is used to obtain a good solution to the student-project allocation problem by maximizing student happiness through the Metropolis algorithm and simulated annealing techniques. | |
| Simulated annealing is used to navigate performance/cost trade-offs when selecting from heterogeneous service offerings for resource allocation in the public cloud. | |
| The paper explains the use of simulated annealing in solving practical tasks, such as the traveling salesman problem. It describes the technique for calculating transition probabilities using Markov chains and shows the capabilities of the simulated annealing algorithm. | |
23 Mar 2023 | Simulated annealing is used as a variant of the Metropolis-Hastings algorithm to solve combinatorial optimization problems by gradually lowering the temperature of the target distribution until the system reaches a global minimum. |
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