Python 3 scripts. Serial implementation of Conformational Space Annealing (CSA), a global optimization method. This method consists of a distance dependent simulated annealing and an evolutionary algorithm (see references below).
This major rewrite allows for the implementation of new Hamiltonian and objective functions using a Base class as template. Crossover and mutations are designed according to the problem to solve. The same for the distance function between solutions.
A random generator class was implemented to facilitate future parallel CSA.
The Spin class reproduces results from:
- Seung-Yeon Kim, Sung Jong Lee, and Jooyoung Lee, Ground-state energy and energy landscape of the Sherrington-Kirkpatrick spin glass, Phys.Rev.B, Vol. 76, 184412-1 - 184412-7 (2007).
This is my open source implementation of CSA as described by (and references within):
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J. Lee, H. A. Scheraga, and S. Rackovsky, J. Comput. Chem. Vol. 18, 1222, (1997)
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J. Insuk et al., Computer Physics Communications, Vol. 223, 28-33, (2018).
TODO: Parallel implementation of CSA global optimization method.
Written by Jose Flores-Canales