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since 2007/10/24

Global problem (GLP)

search for global minimum of a func:
f(x) -> min
subjected to

• Box - bound constraints
  • lb <= x <= ub
    
• Linear inequality constraints
  • A*x <= b
    

(some more constraints will be added if appropriate solver(s) will be connected)

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Note! Typical number of variables for GLP is about 1 ... 15, it will hardly solve other than small-scale problems efficiently.

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OpenOpt GLP example >>>

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GLP solvers

Solver	License	Made by	Are finite box-bounds required	Info	Parameters
galileo	GPL	Donald Goodman	yes	GA-based solver. Cannot handle Ax <= b constraints. Code is included into OO.	population = 15; crossoverRate = 1.0; mutationRate = 0.05; coming useInteger = False (if useInteger = True or 1 then search solution with all integer variables)
(coming) pswarm	BSD	A. I. F. Vaz	Seems like no, mb constraints Ax <= b that provide optimization within finite volume are enough	Download and install pswarm from the URL mentioned (I encountered some troubles in KUBUNTU and have connected pswarm within WinXP), ensure author-provided RunPSwarm.py works ok, and files are inside PYTHONPATH. Documentation says pswarm is capable of using parallel calculations (via MPI) but I don't know is it relevant to Python API. The algorithm combines pattern search and particle swarm. Basically, it applies a directional direct search in the poll step (coordinate search in the pure simple bounds case) and particle swarm in the search step.	social = 2.1