#! /usr/bin/env python

import openturns as ot
import openturns.testing as ott

ot.TESTPREAMBLE()

# 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)
ott.assert_almost_equal(algo.getResult().getOptimalValue(), [3.0])

# 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))
startingPoint = ot.Point(4, 0.0)
algo = ot.SQP(ot.NearestPointProblem(levelFunction, -0.5))
algo.setStartingPoint(startingPoint)
print("algo=", algo)
algo.run()
result = algo.getResult()
print("result=", result)
ott.assert_almost_equal(algo.getResult().getOptimalValue(), [-0.5])