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Description
I observed some weird behavior in my ODE solve with NLNewton(; relax = BackTracking()) where the result is obviously wrong. After some digging I wrote a wrapper of BackTracking where I reject the relaxation when the residu becomes worse w.r.t. to the residu in the full newton step:
@kwdef struct MonitoredBackTracking{B, V}
linesearch::B = BackTracking()
dz_tmp::V = []
z_tmp::V = []
end
"""
MonitoredBackTracing is a thin wrapper of BackTracking, making sure that
the BackTracking relaxation is rejected if it results in a residual increase
"""
function OrdinaryDiffEq.relax!(
dz,
nlsolver::AbstractNLSolver,
integrator::DEIntegrator,
f,
linesearch::MonitoredBackTracking,
)
(; linesearch, dz_tmp, z_tmp) = linesearch
# Store step before relaxation
@. dz_tmp = dz
# Apply relaxation and measure the residu change
@. z_tmp = nlsolver.z + dz
resid_before = resid(z_tmp, integrator, nlsolver, f) # The function resid mimicks this one: https://github.com/SciML/OrdinaryDiffEq.jl/blob/e3af6419b02810ea3b762ab0511cda0e4bcf245c/lib/OrdinaryDiffEqNonlinearSolve/src/newton.jl#L426
relax!(dz, nlsolver, integrator, f, linesearch)
@. z_tmp = nlsolver.z + dz
resid_after = resid(z_tmp, integrator, nlsolver, f)
# If the residu increased due to the relaxation, reject it
if resid_after > resid_before
@. dz = dz_tmp
end
endI found out that many other linesearch methods from LineSearches.jl do a test whether the line is in a descending direction, but BackTracking doesn't, so maybe BackTracking works with that assumption which is sometimes wrong.
But what I find most strange is that these wrong relaxed steps get accepted in all layers: BackTracking, NLNewton, QNDF.