Difference between revisions of "Adaptive simulated annealing (analysis)"
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| + | :[[File:Optimization-Adaptive-simulated-annealing-icon.png]] Adaptive simulated annealing | ||
| + | ;Provider | ||
| + | :[[Institute of Systems Biology]] | ||
| + | ;Class | ||
| + | :{{Class|ru.biosoft.analysis.optimization.methods.ASAOptMethod}} | ||
| + | ;Plugin | ||
| + | :[[Ru.biosoft.analysis.optimization (plugin)|ru.biosoft.analysis.optimization (Common methods of data optimization analysis plug-in)]] | ||
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=== Adaptive simulated annealing (ASA) === | === Adaptive simulated annealing (ASA) === | ||
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[[Category:Analyses]] | [[Category:Analyses]] | ||
[[Category:Optimization (analyses group)]] | [[Category:Optimization (analyses group)]] | ||
| + | [[Category:ISB analyses]] | ||
[[Category:Autogenerated pages]] | [[Category:Autogenerated pages]] | ||
Latest revision as of 16:16, 14 June 2013
- Analysis title
Adaptive simulated annealing
- Provider
- Institute of Systems Biology
- Class
ASAOptMethod- Plugin
- ru.biosoft.analysis.optimization (Common methods of data optimization analysis plug-in)
[edit] Adaptive simulated annealing (ASA)
ASA algorithm1 is developed to statistically find the best global fit of a nonlinear non-convex cost-function over a D-dimensional space. It is argued that this algorithm permits an annealing schedule for "temperature" T decreasing exponentially in annealing-time k, T = T0exp(-ck(1/D)). The introduction of re-annealing also permits adaptation to changing sensitivities in the multi-dimensional parameter-space. This annealing schedule is faster than fast Cauchy annealing, where T = T0/k, and much faster than Boltzmann annealing, where T = T0/ln k.
[edit] References
- L Ingber, "Adaptive simulated annealing (ASA): Lessons learned." Control and Cybernetics, Vol. 25, No. 1, pp. 33-54, 1996.
