else:
sep = ","
if m.fabs(point[i]) < eps:
oss += sep + format % m.fabs(point[i])
else:
oss += sep + format % point[i]
sep = ","
oss += "]"
return oss
# linear
levelFunction = ot.SymbolicFunction(
["x1", "x2", "x3", "x4"], ["x1+2*x2-3*x3+4*x4"])
startingPoint = ot.Point(4, 0.0)
algo = ot.Cobyla(ot.NearestPointProblem(levelFunction, 3.0))
algo.setStartingPoint(startingPoint)
print('algo=', algo)
algo.run()
result = algo.getResult()
print('x^=', printPoint(result.getOptimalPoint(), 4))
print('f(x^)=', printPoint(result.getOptimalValue(), 4))
print('lambda^=', printPoint(result.computeLagrangeMultipliers(), 4))
# non-linear
levelFunction = ot.SymbolicFunction(
["x1", "x2", "x3", "x4"], ["x1*cos(x1)+2*x2*x3-3*x3+4*x3*x4"])
startingPoint = ot.Point(4, 0.0)
algo = ot.Cobyla(ot.NearestPointProblem(levelFunction, 3.0))
algo.setStartingPoint(startingPoint)
algo.setMaximumEvaluationNumber(400)
import openturns as ot
ot.TESTPREAMBLE()
ot.PlatformInfo.SetNumericalPrecision(3)
# linear
levelFunction = ot.SymbolicFunction(["x1", "x2", "x3", "x4"],
["x1+2*x2-3*x3+4*x4"])
# Add a finite difference gradient to the function
myGradient = ot.NonCenteredFiniteDifferenceGradient(
1e-7, levelFunction.getEvaluation())
print("myGradient = ", repr(myGradient))
# Substitute the gradient
levelFunction.setGradient(ot.NonCenteredFiniteDifferenceGradient(myGradient))
startingPoint = ot.Point(4, 0.0)
algo = ot.SQP(ot.NearestPointProblem(levelFunction, 3.0))
algo.setStartingPoint(startingPoint)
print('algo=', algo)
algo.run()
result = algo.getResult()
print('result=', result)
# non-linear
levelFunction = ot.SymbolicFunction(["x1", "x2", "x3", "x4"],
["x1*cos(x1)+2*x2*x3-3*x3+4*x3*x4"])
# Add a finite difference gradient to the function,
# needs it
myGradient = ot.NonCenteredFiniteDifferenceGradient(
1e-7, levelFunction.getEvaluation())
# Substitute the gradient
levelFunction.setGradient(ot.NonCenteredFiniteDifferenceGradient(myGradient))
from __future__ import print_function
import openturns as ot
ot.TESTPREAMBLE()
levelFunction = ot.SymbolicFunction(["x1", "x2", "x3", "x4"],
["x1+2*x2-3*x3+4*x4"])
# Add a finite difference gradient to the function, as Abdo Rackwitz algorithm
# needs it
myGradient = ot.NonCenteredFiniteDifferenceGradient(
1e-7, levelFunction.getEvaluation())
print("myGradient = ", repr(myGradient))
# Substitute the gradient
levelFunction.setGradient(ot.NonCenteredFiniteDifferenceGradient(myGradient))
startingPoint = [0.0] * 4
algo = ot.AbdoRackwitz(ot.NearestPointProblem(levelFunction, 3.0))
algo.setStartingPoint(startingPoint)
algo.run()
print("result = ", algo.getResult())
levelFunction = ot.SymbolicFunction(["x1", "x2", "x3", "x4"],
["x1*cos(x1)+2*x2*x3-3*x3+4*x3*x4"])
# Add a finite difference gradient to the function, as Abdo Rackwitz algorithm
# needs it
myGradient = ot.NonCenteredFiniteDifferenceGradient(
1e-7, levelFunction.getEvaluation())
print("myGradient = ", repr(myGradient))
# Substitute the gradient
levelFunction.setGradient(ot.NonCenteredFiniteDifferenceGradient(myGradient))
startingPoint = [0.0] * 4
algo = ot.AbdoRackwitz(ot.NearestPointProblem(levelFunction, -0.5))
