def test_nlrow():
# create nonlinear model
m = Model("nlrow")
# add heuristic to interrupt solve: the methods we wanna test can only be called in solving stage
heuristic = MyHeur()
m.includeHeur(heuristic, "PyHeur", "heur to interrupt", "Y", timingmask=SCIP_HEURTIMING.BEFORENODE)
# create variables
x = m.addVar(name="x", lb=-3, ub=3, obj=-1)
y = m.addVar(name="y", lb=-3, ub=3, obj=-1)
# create constraints
m.addCons(1*x + 2*y + 3 * x**2 + 4*y**2 + 5*x*y <= 6)
m.addCons(7*x**2 + 8*y**2 == 9)
m.addCons(10*x + 11*y <= 12)
# optimize without presolving
m.setPresolve(SCIP_PARAMSETTING.OFF)
m.optimize()
# check whether NLP has been constructed and there are 3 nonlinear rows that match the above constraints
assert m.isNLPConstructed()
assert m.getNNlRows() == 3
# collect nonlinear rows
nlrows = m.getNlRows()
# the nlrow that corresponds to the linear (third) constraint is added before the nonlinear rows,
# because Initsol of the linear conshdlr gets called first
# therefore the ordering is: nlrows[0] is for constraint 3, nlrows[1] is for constraint 1,
# nlrows[2] is for constraint 2
# check first nonlinear row that represents constraint 3
assert nlrows[0].getLhs() == -m.infinity()
assert nlrows[0].getRhs() == 12
linterms = nlrows[0].getLinearTerms()
assert len(linterms) == 2
assert str(linterms[0][0]) == "t_x"
assert linterms[0][1] == 10
assert str(linterms[1][0]) == "t_y"
assert linterms[1][1] == 11
linterms = nlrows[1].getLinearTerms()
assert len(linterms) == 2
assert str(linterms[0][0]) == "t_x"
assert linterms[0][1] == 1
assert str(linterms[1][0]) == "t_y"
assert linterms[1][1] == 2
# check third nonlinear row that represents constraint 2
assert nlrows[2].getLhs() == 9
assert nlrows[2].getRhs() == 9
linterms = nlrows[2].getLinearTerms()
assert len(linterms) == 0
def test_nlrow():
# create nonlinear model
m = Model("nlrow")
# create variables
x = m.addVar(name="x", lb=-3, ub=3, obj=-1)
y = m.addVar(name="y", lb=-3, ub=3, obj=-1)
# create constraints
m.addCons(1 * x + 2 * y + 3 * x**2 + 4 * y**2 + 5 * x * y <= 6)
m.addCons(7 * x**2 + 8 * y**2 == 9)
m.addCons(10 * x + 11 * y <= 12)
# optimize without presolving
m.setPresolve(SCIP_PARAMSETTING.OFF)
m.optimize()
# check whether NLP has been constructed and there are 3 nonlinear rows that match the above constraints
assert m.isNLPConstructed()
assert m.getNNlRows() == 3
# collect nonlinear rows
nlrows = m.getNlRows()
# check first nonlinear row
assert nlrows[0].getLhs() == -m.infinity()
assert nlrows[0].getRhs() == 6
linterms = nlrows[0].getLinearTerms()
assert len(linterms) == 2
assert str(linterms[0][0]) == "t_x"
assert linterms[0][1] == 1
assert str(linterms[1][0]) == "t_y"
assert linterms[1][1] == 2
quadterms = nlrows[0].getQuadraticTerms()
assert len(quadterms) == 3
assert str(quadterms[0][0]) == "t_x"
assert str(quadterms[0][1]) == "t_x"
assert quadterms[0][2] == 3
assert str(quadterms[1][0]) == "t_y"
assert str(quadterms[1][1]) == "t_y"
assert quadterms[1][2] == 4
assert str(quadterms[2][0]) == "t_x"
assert str(quadterms[2][1]) == "t_y"
assert quadterms[2][2] == 5
# check second nonlinear row
assert nlrows[1].getLhs() == 9
assert nlrows[1].getRhs() == 9
linterms = nlrows[1].getLinearTerms()
assert len(linterms) == 0
quadterms = nlrows[1].getQuadraticTerms()
assert len(quadterms) == 2
assert str(quadterms[0][0]) == "t_x"
assert str(quadterms[0][1]) == "t_x"
assert quadterms[0][2] == 7
assert str(quadterms[1][0]) == "t_y"
assert str(quadterms[1][1]) == "t_y"
assert quadterms[1][2] == 8
# check third nonlinear row
assert nlrows[2].getLhs() == -m.infinity()
assert nlrows[2].getRhs() == 12
linterms = nlrows[2].getLinearTerms()
assert len(linterms) == 2
assert str(linterms[0][0]) == "t_x"
assert linterms[0][1] == 10
assert str(linterms[1][0]) == "t_y"
assert linterms[1][1] == 11
quadterms = nlrows[2].getQuadraticTerms()
assert len(quadterms) == 0
