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#!/usr/bin/env python
import openturns as ot
import openturns.testing as ott
# Define the problems based on Rosebrock function
rosenbrock = ot.SymbolicFunction(["x1", "x2"], ["(1-x1)^2+100*(x2-x1^2)^2"])
unboundedProblem = ot.OptimizationProblem(rosenbrock)
notConstrainingBounds = ot.Interval([-5.0, -5.0], [5.0, 5.0])
constrainingBounds = ot.Interval([0.0, -2.0], [5.0, 0.5])
start = [3.0, -1.5]
unboundedPref = [1.0, 1.0]
boundedPref = [0.70856, 0.5]
for bound in [False, True]:
for bounds in [ot.Interval(), notConstrainingBounds, constrainingBounds]:
problem = ot.OptimizationProblem(rosenbrock)
problem.setBounds(bounds)
for algoName in ["cg", "bfgs", "lbfgs", "newton", "trust_region"]:
if algoName == "trust_region" and problem.hasBounds():
continue
algo = ot.Dlib(problem, algoName)
algo.setStartingPoint(start)
algo.setMaximumIterationNumber(10000)
algo.setMaximumCallsNumber(100000)
algo.run()
result = algo.getResult()
x = result.getOptimalPoint()
y = result.getOptimalValue()
print(f"bounds={problem.hasBounds()} algo={algoName} x^={x} y^={y}")
if bounds == constrainingBounds:
ott.assert_almost_equal(x, boundedPref, 5e-2)
else:
ott.assert_almost_equal(x, unboundedPref, 5e-2)
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