def test_std_vector_run():
params_dict = {
'General': {
'Secant': {
'Type': 'Limited-Memory BFGS',
'Maximum Storage': 5
}
},
'Step': {
'Type': 'Line Search',
'Line Search': {
'Descent Method': {
'Type': 'Quasi-Newton Method'
}
}
},
'Status Test': {
'Gradient Tolerance': 1e-15,
'Relative Gradient Tolerance': 1e-10,
'Step Tolerance': 1e-16,
'Relative Step Tolerance': 1e-10,
'Iteration Limit': 10
}
}
params = ROL.ParameterList(params_dict, "Parameters")
algo = ROL.Algorithm("Line Search", params)
x = ROL.StdVector(2)
x[0] = -1.0
x[1] = 2.0
algo.run(x, obj)
# Check answer to 8 decimal places
assert round(x[0] - 1.0, 8) == 0.0
assert round(x[1], 8) == 0.0
inner_product = L2Inner()
obj = Objective(inner_product)
u = Function(V, name="velocity").assign(vp)
opt = FeVector(u.vector(), inner_product)
# Add control bounds to the problem (uses more RAM)
xlo = Function(V)
xlo.interpolate(Constant(1.0))
x_lo = FeVector(xlo.vector(), inner_product)
xup = Function(V)
xup.interpolate(Constant(5.0))
x_up = FeVector(xup.vector(), inner_product)
bnd = ROL.Bounds(x_lo, x_up, 1.0)
algo = ROL.Algorithm("Line Search", params)
algo.run(opt, obj, bnd)
if comm.ensemble_comm.rank == 0:
File("res.pvd", comm=comm.comm).write(vp)
if COMM_WORLD.rank == 0:
func.close()
mem.close()
V0)
d = LA(d.vector(), dot_product)
x.checkVector(d, g)
jd = Function(V0)
jd = LA(jd.vector(), dot_product)
lower = interpolate(Constant(0.0), V0)
lower = LA(lower.vector(), dot_product)
upper = interpolate(Constant(1.0), V0)
upper = LA(upper.vector(), dot_product)
# Instantiate Objective class for poisson problem
obj = ObjR(dot_product)
obj.checkGradient(x, d, 7, 1)
volConstr = VolConstraint(dot_product)
volConstr.checkApplyJacobian(x, d, jd, 6, 1)
volConstr.checkAdjointConsistencyJacobian(v, d, x)
with open('input.xml', 'r') as myfile:
parametersXML = myfile.read().replace('\n', '')
set_log_level(30)
params = ROL.ParameterList(parametersXML)
bound_constraint = ROL.BoundConstraint(lower, upper, 1.0)
alg2 = ROL.Algorithm("Augmented Lagrangian", params)
penaltyParam = 1
augLag = ROL.AugmentedLagrangian(obj, volConstr, l, penaltyParam, x, c, params)
alg2.run(x, l, augLag, volConstr, bound_constraint)