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since 2007/10/24

The page is obsolete, we have moved to openopt.org

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! GLP solvers are not capable of handling problems with nVars more than ~ 150...300 efficiently. Both galileo and pswarm don't use user-supplied derivatives.

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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; useInteger = False (if useInteger = True or 1 then search solution with all integer variables)
pswarm (latest OO tarball requires v >= 1.3)	LGPL	A. I. F. Vaz	Seems like no, mb constraints Ax <= b that provide optimization within finite volume are enough	Can handle user-provided x0. Download and install pswarm from the URL mentioned, ensure author-provided RunPSwarm.py works ok, and pswarm_py.so is 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. See also: resent paper on PSwarm published at optimization-online.org.	social = 0.5; cognitial = 0.5; fweight = 0.4; iweight = 0.9; size = 42; tol = 1e-5; ddelta = 0.5; idelta = 2.0