define `upto_gradient` for `MeritObjective`

[longemen3000]

something like this?

function NLSolvers.upto_gradient(meritobj::NLSolvers.MeritObjective, ∇f, x)
    neq = meritobj.prob
    G = neq.R.F(∇f, x)
    F =  (norm(G)^2) / 2
    return F,G
end

with that, NLSolvers.solve(prob,x0,LineSearch(Newton(),HZAW())) seems to work
EDIT: no it doesn't. but at least it hits an error in HZAW instead:
MethodError: no method matching isfinite(::NamedTuple{(:ϕ, :Fx), Tuple{Float64, StaticArrays.MVector{2, Float64}}})

NLSolvers.jl/src/globalization/linesearches/hagerzhangline.jl

Line 47 in 45e246b

while !isfinite(φc) && iter <= maxiter

[longemen3000]

is this related?

NLSolvers.jl/src/nlsolve/linesearch/newton.jl

Lines 79 to 94 in f1ad8c0

	# Need to restrict to static and backtracking here because we don't allow
	# for methods that calculate the gradient of the line objective.
	#
	# For non-linear systems of equations we choose the sum-of-
	# squares merit function. Some useful things to remember is:
	#
	# f(y) = 1/2*|| F(y) ||^2 =>
	# ∇_df = -d'*J(x)'*F(x)
	#
	# where we remember the notation x means the current iterate and y is any
	# proposal. This means that if we step in the Newton direction such that d
	# is defined by
	#
	# J(x)*d = -F(x) => -d'*J(x)' = F(x)' =>
	# ∇_df = -F(x)'*F(x) = -f(x)*2
	#