Type:

Other

Description:

This teaching module introduces stochastic approaches to finding optimum solutions for adequately defined systems. Because these approaches are intrinsically random, large numbers of random samples are typically required to find robust optima. This results in either long single simulation runs, or the need for multiple replicated simulations considered as an ensemble, or both. Monte Carlo, simulated annealing and genetic algorithm approaches to optimization are introduced in this module and applied to a few example problems, and parallelization strategies and their resulting performance gains are assessed.

Subjects:

  • Computer Science > General
  • Mathematics > General

Education Levels:

  • Grade 1
  • Grade 2
  • Grade 3
  • Grade 4
  • Grade 5
  • Grade 6
  • Grade 7
  • Grade 8
  • Grade 9
  • Grade 10
  • Grade 11
  • Grade 12

Keywords:

Informal Education,Higher Education,NSDL,Computational Science,Graduate/Professional,NSDL_SetSpec_ncs-NSDL-COLLECTION-000-003-112-055,Complex Systems,Computer Science,Vocational/Professional Development Education,Mathematics,oai:nsdl.org:2200/20120503185407868T,Physics,Computing and Information

Language:

English

Access Privileges:

Public - Available to anyone

License Deed:

Creative Commons Attribution Non-Commercial Share Alike

Collections:

None
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