/usr/include/openturns/swig/SimulatedAnnealingLHS_doc.i is in libopenturns-dev 1.9-5.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | %define OT_SimulatedAnnealing_doc
"LHS optimization using simulated annealing.
Performs the optimization of an LHS using simulated annealing algorithm.
Available constructors:
SimulatedAnnealingLHS(*lhsDesign*)
SimulatedAnnealingLHS(*lhsDesign, profile*)
SimulatedAnnealingLHS(*lhsDesign, profile, spaceFilling*)
SimulatedAnnealingLHS(*initialDesign, distribution*)
SimulatedAnnealingLHS(*initialDesign, distribution, profile*)
SimulatedAnnealingLHS(*initialDesign, distribution, profile, spaceFilling*)
Parameters
----------
lhsDesign : :class:`~openturns.LHSExperiment`
Factory that generate designs
initialDesign : 2d-array sequence
Initial design to be optimized
distribution : :class:`~openturns.Distribution`
Distribution of designs
profile : :class:`~ot.TemperatureProfile`
Temperature profile used by the simulated annealing algorithm
Default one is GeometricProfile
spaceFilling : :class:`~ot.SpaceFilling`
Criterion to be optimized
Default one is SpaceFillingMinDist
Notes
-----
With the first constructor, the initial design is generated thanks to lhsDesign. With the second usage, we fix it.
Starting from this design, a new design is obtained by permuting one random coordinate of two randomly chosen elements; by construction, this design is also an LHS design.
If the new design is better than the previous one, it is kept.
If it is worse, it may anyway be kept with some probability, which depends on how these designs compare, but also on a temperature profile
T which decreases over time. This means that jumping away from local extrema becomes less probable over time.
Examples
--------
>>> import openturns as ot
>>> dimension = 3
>>> size = 100
>>> # Build standard randomized LHS algorithm
>>> distribution = ot.ComposedDistribution([ot.Uniform(0.0, 1.0)]*dimension)
>>> lhs = ot.LHSExperiment(distribution, size)
>>> lhs.setAlwaysShuffle(True) # randomized
>>> # Defining space fillings
>>> spaceFilling = ot.SpaceFillingC2()
>>> # Geometric profile
>>> geomProfile = ot.GeometricProfile(10.0, 0.95, 2000)
>>> # Simulated Annealing LHS with geometric temperature profile, C2 optimization
>>> optimalLHSAlgorithm = ot.SimulatedAnnealingLHS(lhs, geomProfile, spaceFilling)"
%enddef
%feature("docstring") OT::SimulatedAnnealingLHS
OT_SimulatedAnnealing_doc
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