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| | == Simulated Annealing == | | == Simulated Annealing == |
| − | | + | <font color="red"> More to come on this subject! </font> |
| − | explanation goes here
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| − | === ParSA ===
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| − | Here [http://www.phys.uconn.edu/~demas/diamonds/parSA1/ParSALibDoc.pdf] is the link the the ParSA documentation.
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| − | The ParSA (Parallel Simulated Annealing) library is a set of classes written in C++ that can be used to solve optimization problems via a process know as simulated annealing.
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| − | The ParSA library contains many different types of
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| − | The Equation for convergence speed is:
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| − | {|width="50%"
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| − | <math>P\left(X_n \notin Cost_{min}\right) \approx \left(\frac{K}{n}\right)^\alpha</math> | |
| − | |align="center" width="80"|(1)
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| − | |}
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| − | Where ''K'' and <math>\alpha</math> are problem specific constants and <math>X_n</math> is a solution of length ''n''. Using equation (1) and test runs on smaller problems of lower order, ''K'' and <math>\alpha</math> can be determined. Along with some suggestions provided in the ParSA documentation, progress can be made towards finding higher quality solutions at a much faster rate.
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| − | {|width="50%"
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| − | <math>\ln P = \alpha \left(\ln K - \ln n\right)</math>
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| − | |align="center" width="80"|(2)
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| − | |}
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| − | The equation for warming temperature in the Aarts scheduler:
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| − | {|width="50%"
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| − | <math>T=\bar{\Delta C^{(|)}}\left(\ln \frac{m_2}{m_2\chi_0-(1-\chi_0)m_1}\right)^{-1}</math>
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| − | |align="center" width="80"|(3)
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| − | |}
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| − | === Table of Proposed Runs ===
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| − | <u>Clustering</u>
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| − | SA Clustering Solver
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| − | Clustering Scheduler
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| − | * SA Aarts
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| − | * SA Easy Scheduler
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| − | <u>Multiple Independent Runs (M.I.R.)</u>
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| − | MIR_Solver
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| − | MIR_Scheduler
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| − | Use Combinations of the Different Solver/Scheduler Classes
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| | == References == | | == References == |