diff --git a/src/fixedpoints/anderson.jl b/src/fixedpoints/anderson.jl
index 2ba5bde..18cf08a 100644
--- a/src/fixedpoints/anderson.jl
+++ b/src/fixedpoints/anderson.jl
@@ -94,7 +94,7 @@ options = NEqOptions(maxiter=maxiter)
 
     Gold = copy(Gx)
     Fold = copy(Fx)
-    γv0 = zeros(effective_memory)
+    γv0 = zeros(eltype(Gx),memory)
     iter = 0
     while iter < options.maxiter
         iter += 1
diff --git a/src/nlsolve/linesearch/newton.jl b/src/nlsolve/linesearch/newton.jl
index cb3a6da..0fb1300 100644
--- a/src/nlsolve/linesearch/newton.jl
+++ b/src/nlsolve/linesearch/newton.jl
@@ -8,8 +8,6 @@ function solve(
     options = NEqOptions(),
     state = init(problem, method; x),
 )
-    t0 = time()
-
     # Unpack
     scheme, linesearch = modelscheme(method), algorithm(method)
     # Unpack important objectives
@@ -17,15 +15,29 @@ function solve(
     FJ = problem.R.FJ
     # Unpack state
     z, d, Fx, Jx = state
-    z .= x
-    T = eltype(Fx)
-
-
+    T = eltype(x)
     # Set up MeritObjective. This defines the least squares
     # objective for the line search.
     merit = MeritObjective(problem, F, FJ, Fx, Jx, d)
     meritproblem =
-        OptimizationProblem(merit, nothing, Euclidean(0), nothing, mstyle(problem), nothing)
+        OptimizationProblem(merit, bounds(problem), _manifold(problem), nothing, mstyle(problem), nothing)
+
+    opt_options = OptimizationOptions(options)
+    res = solve(meritproblem, x, method, opt_options)
+
+    F0 = res.info.∇f0
+    ρF0, ρ2F0 = norm(F0, Inf), norm(F0, 2)
+    newinfo = (
+        solution = solution(res),
+        best_residual = res.info.minimum,
+        ρs = T(NaN),
+        ρF0 = ρF0,
+        ρ2F0 = ρ2F0,
+        time = res.info.time,
+        iter = res.info.iter,
+    )
+
+    return ConvergenceInfo(method, newinfo, options)
 
     # Evaluate the residual and Jacobian
     Fx, Jx = FJ(Fx, Jx, x)
diff --git a/src/nlsolve/problem_types.jl b/src/nlsolve/problem_types.jl
index 3bdb14d..9cd8ec5 100644
--- a/src/nlsolve/problem_types.jl
+++ b/src/nlsolve/problem_types.jl
@@ -60,6 +60,7 @@ struct NEqOptions{T,Tmi,Call}
     f_reltol::T
     maxiter::Tmi
     callback::Call
+    show_trace::Bool
 end
 NEqOptions(;
     f_limit = 0.0,
@@ -67,7 +68,26 @@ NEqOptions(;
     f_reltol = 1e-12,
     maxiter = 10^4,
     callback = nothing,
-) = NEqOptions(f_limit, f_abstol, f_reltol, maxiter, callback)
+    show_trace = false,
+) = NEqOptions(f_limit, f_abstol, f_reltol, maxiter, callback, show_trace)
+
+function OptimizationOptions(neq::NEqOptions)
+    return OptimizationOptions(;
+    x_abstol = 0.0,
+    x_reltol = 0.0,
+    x_norm = x -> norm(x, Inf),
+    g_abstol = 2*neq.f_abstol,
+    g_reltol = 2*neq.f_reltol,
+    g_norm = x -> norm(x, Inf),
+    f_limit = neq.f_limit,
+    f_abstol = 0.0,
+    f_reltol = 0.0,
+    nm_tol = 1e-8,
+    maxiter = neq.maxiter,
+    show_trace = neq.show_trace,
+)
+end
+
 function Base.show(io::IO, ci::ConvergenceInfo{<:Any,<:Any,<:NEqOptions})
     opt = ci.options
     info = ci.info
@@ -131,3 +151,66 @@ function Base.show(io::IO, ci::ConvergenceInfo{<:Any,<:Any,<:NEqOptions})
     println(io, "  Seconds run:   $(@sprintf("%.2e", info.time))")
     println(io, "  Iterations:    $(info.iter)")
 end
+
+function upto_gradient(merit::MeritObjective, Fx, x)
+    upto_gradient(merit.prob, Fx, x)
+    #merit.F .= Fx
+    #return res
+end
+upto_gradient(neq::NEqProblem, Fx, x) =
+    upto_gradient(neq.R, Fx, x)
+
+function upto_gradient(vo::VectorObjective, Fx, x)
+    value(vo,Fx,x)
+    obj = norm(Fx)^2/2
+    return obj, Fx
+end
+
+function upto_hessian(merit::MeritObjective, ∇f, ∇²f, x)
+    upto_hessian(merit.prob, ∇f, ∇²f, x)
+end
+
+upto_hessian(neq::NEqProblem, ∇f, ∇²f, x) =
+    upto_hessian(neq.R, ∇f, ∇²f, x)
+
+function upto_hessian(vo::VectorObjective, ∇f, ∇²f, x)
+    Fx, Jx = vo.FJ(∇f, ∇²f, x)
+    obj = norm(Fx)^2/2
+    return obj, Fx, Jx
+end
+
+function LineObjective(obj::TP,∇fz::T1,z::T2,x::T2,d::T2,φ0::T3,dφ0::T3) where {TP<:(NLSolvers.OptimizationProblem{<:NLSolvers.MeritObjective}),T1,T2,T3}
+    return LineObjective(obj.objective,∇fz,z,x,d,φ0,dφ0)
+end
+
+function LineObjective!(obj::TP,∇fz::T1,z::T2,x::T2,d::T2,φ0::T3,dφ0::T3) where {TP<:(NLSolvers.OptimizationProblem{<:NLSolvers.MeritObjective}),T1,T2,T3}
+    return LineObjective!(obj.objective,∇fz,z,x,d,φ0,dφ0)
+end
+
+function ls_has_gradient(obj::MeritObjective)
+    if obj.prob.R.Jv === nothing
+        throw(error("You need to provide Jv for using LineSearch with gradient information."))
+    end
+    return nothing
+end
+
+function ls_upto_gradient(merit::MeritObjective, Fx, x)
+    upto_gradient(merit.prob, Fx, x)
+end
+upto_gradient(neq::NEqProblem, Fx, x) =
+    upto_gradient(neq.R, Fx, x)
+
+function upto_gradient(vo::VectorObjective, Fx, x)
+    vo.Jv(x)
+    value(vo,Fx,x)
+    obj = norm(Fx)^2/2
+    return obj, Fx
+end
+
+
+_manifold(merit::NLSolvers.MeritObjective) = _manifold(merit.prob)
+
+bounds(neq::NEqProblem) = neq.bounds
+
+
+
diff --git a/src/objectives.jl b/src/objectives.jl
index ffff7ce..c1682c1 100644
--- a/src/objectives.jl
+++ b/src/objectives.jl
@@ -100,6 +100,10 @@ end
 
 ## If prob is a NEqProblem, then we can just dispatch to least squares MeritObjective
 # if fast JacVec exists then maybe even line searches that updates the gradient can be used??? 
+ls_has_gradient(prob) = nothing
+ls_upto_gradient(prob,∇f,x) = upto_gradient(prob,∇f,x)
+ls_value(prob,z) = (value(prob,z),z)
+
 struct LineObjective!{TP,T1,T2,T3}
     prob::TP
     ∇fz::T1
@@ -109,15 +113,19 @@ struct LineObjective!{TP,T1,T2,T3}
     φ0::T3
     dφ0::T3
 end
+
 function (le::LineObjective!)(λ)
     z = retract!(_manifold(le.prob), le.z, le.x, le.d, λ)
-    ϕ = value(le.prob, z)
-    (ϕ = ϕ, z = z)
+    ϕ,z = ls_value(le.prob, z)
+    return (ϕ = ϕ, z = z)
 end
+
 function (le::LineObjective!)(λ, calc_grad::Bool)
-    f, g = upto_gradient(le.prob, le.∇fz, retract!(_manifold(le.prob), le.z, le.x, le.d, λ))
+    ls_has_gradient(le.prob)
+    f, g = ls_upto_gradient(le.prob, le.∇fz, retract!(_manifold(le.prob), le.z, le.x, le.d, λ))
     (ϕ = f, dϕ = real(dot(g, le.d))) # because complex dot might not have exactly zero im part and it's the wrong type
 end
+
 struct LineObjective{TP,T1,T2,T3}
     prob::TP
     ∇fz::T1
@@ -127,16 +135,16 @@ struct LineObjective{TP,T1,T2,T3}
     φ0::T3
     dφ0::T3
 end
+
 function (le::LineObjective)(λ)
     z = retract(_manifold(le.prob), le.x, le.d, λ)
-    _value = value(le.prob, z)
-    if le.prob.objective isa MeritObjective
-        return (ϕ = _value.ϕ, _value.Fx)
-    end
-    (ϕ = _value,)
+    ϕ,z = ls_value(le.prob, z)
+    return (ϕ = ϕ, z = z)
 end
+
 function (le::LineObjective)(λ, calc_grad::Bool)
-    f, g = upto_gradient(le.prob, le.∇fz, retract(_manifold(le.prob), le.x, le.d, λ))
+    ls_has_gradient(le.prob)
+    f, g = ls_upto_gradient(le.prob, le.∇fz, retract(_manifold(le.prob), le.x, le.d, λ))
     (ϕ = f, dϕ = real(dot(g, le.d))) # because complex dot might not have exactly zero im part and it's the wrong type
 end
 
@@ -156,7 +164,13 @@ struct MeritObjective{TP,T1,T2,T3,T4,T5}
 end
 function value(mo::MeritObjective, x)
     Fx = mo.F(mo.Fx, x)
-    (ϕ = (norm(Fx)^2) / 2, Fx = Fx)
+    return norm(Fx)^2 / 2
+    #(ϕ = (norm(Fx)^2) / 2, Fx = Fx)
+end
+
+function ls_value(mo::MeritObjective, x)
+    Fx = mo.F(mo.Fx, x)
+    return (norm(Fx)^2 / 2,Fx)
 end
 
 struct LsqWrapper{Tobj,TF,TJ} <: ObjWrapper
diff --git a/src/optimize/problem_types.jl b/src/optimize/problem_types.jl
index 856bf1f..c674084 100644
--- a/src/optimize/problem_types.jl
+++ b/src/optimize/problem_types.jl
@@ -223,7 +223,7 @@ OptimizationOptions(;
     maxiter,
     show_trace,
 )
-
+#=
 struct MinResults{Tr,Tc<:ConvergenceInfo,Th,Ts,To}
     res::Tr
     conv::Tc
@@ -251,7 +251,7 @@ function Base.show(io::IO, mr::MinResults)
     println(io)
     println(io, " * Convergence measures\n")
     show(io, convinfo(mr))
-end
+end =#
 #=
 * Work counters
   Seconds run:   0  (vs limit Inf)
@@ -260,6 +260,18 @@ end
   ∇f(x) calls:   53
 =#
 
+function _identity(v::Vector{T}) where T
+    out = Matrix{T}(undef, length(v), length(v))
+    out .= false
+    for i in 1:length(v)
+        out[i,i] = true
+    end
+    return out
+end
+
+function _identity(x)
+     return I + x .* x' .* false
+end
 function prepare_variables(prob, approach, x0, ∇fz, B)
     objective = prob.objective
     z = x0
@@ -278,7 +290,7 @@ function prepare_variables(prob, approach, x0, ∇fz, B)
         else
             # Construct a matrix on the correct form and of the correct type
             # with the content of I_{n,n}
-            B = I + abs.(0 * x * x')
+            B = _identity(x)
         end
     end
     # first evaluation
@@ -303,7 +315,7 @@ end
 function x_converged(x, z, options)
     x_converged = false
     if x !== nothing # if not calling from initial_converged
-        y = x .- z
+        y = (xi-zi for (xi,zi) in zip(x,z)) #do not allocate an additional vector
         ynorm = options.x_norm(y)
         x_converged = x_converged || ynorm ≤ options.x_abstol
         x_converged = x_converged || ynorm ≤ options.x_norm(x) * options.x_reltol
diff --git a/src/quasinewton/update_qn.jl b/src/quasinewton/update_qn.jl
index 3129ba4..7371e6f 100644
--- a/src/quasinewton/update_qn.jl
+++ b/src/quasinewton/update_qn.jl
@@ -8,14 +8,14 @@ function update_obj!(problem, s, y, ∇fx, z, ∇fz, B, scheme, scale = nothing)
     # Update B
     if scale == nothing
         if isa(scheme.approx, Direct)
-            yBy = dot(y, B * y)
+            yBy = dot(y, B, y)
             if !iszero(yBy)
                 Badj = dot(y, s) / yBy .* B
             end
         else
             ys = dot(y, s)
             if !iszero(ys)
-                Badj = dot(y, B * y) / ys .* B
+                Badj = dot(y, B, y) / ys .* B
             else
                 return fz, ∇fz, B, s, y
             end