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#!/usr/bin/env python
import openturns as ot
import openturns.testing as ott
import sys
def progress(percent):
sys.stderr.write("-- progress=" + str(percent) + "%\n")
def stop():
sys.stderr.write("-- stop?\n")
return False
# List available algorithms
print(ot.Bonmin.GetAlgorithmNames())
# Definition of objective function
objectiveFunction = ot.SymbolicFunction(["x0", "x1", "x2", "x3"], ["-x0 -x1 -x2"])
# Definition of variables bounds
bounds = ot.Interval([0] * 4, [1, 1e6, 1e6, 5])
# Definition of constraints
# Constraints in OpenTURNS are defined as g(x) = 0 and h(x) >= 0
# No equality constraint -> nothing to do
# Inequality constraints:
h = ot.SymbolicFunction(
["x0", "x1", "x2", "x3"],
["-(x1-1/2)^2 - (x2-1/2)^2 + 1/4", "-x0 + x1", "-x0 - x2 - x3 + 2"],
)
# Definition of variables types
variablesType = [
ot.OptimizationProblemImplementation.BINARY,
ot.OptimizationProblemImplementation.CONTINUOUS,
ot.OptimizationProblemImplementation.CONTINUOUS,
ot.OptimizationProblemImplementation.INTEGER,
]
# Setting up Bonmin problem
problem = ot.OptimizationProblem(objectiveFunction)
problem.setBounds(bounds)
problem.setVariablesType(variablesType)
problem.setInequalityConstraint(h)
algo = ot.Bonmin(problem)
algo.setStartingPoint([0.0] * 4)
algo.setMaximumCallsNumber(10000)
algo.setMaximumIterationNumber(1000)
# algo.setProgressCallback(progress)
# algo.setStopCallback(stop)
for name in ot.Bonmin.GetAlgorithmNames():
print(f"-- {name} algorithm...")
algo.setAlgorithmName(name)
algo.run()
result = algo.getResult()
x_star = result.getOptimalPoint()
print(f"x*={x_star}")
y_star = result.getOptimalValue()
neval = result.getCallsNumber()
print(f"f(x*)={y_star} neval={neval}")
ott.assert_almost_equal(result.getOptimalPoint(), [1, 1, 0.5, 0], 1, 1e-2)
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