add skip_sigma kwarg

Author: MaxenceGollier

src/LMModel.jl

@@ -39,11 +39,12 @@ function LMModel(J::Jac, F::V, σ::T, xk::V) where {T, V, Jac}
   return LMModel(J, F, v, xk, σ, meta, Counters())
 end
 
-function NLPModels.obj(nlp::LMModel, x::AbstractVector{T}) where {T}
+function NLPModels.obj(nlp::LMModel, x::AbstractVector{T}; skip_sigma = false) where {T}
   @lencheck nlp.meta.nvar x
   increment!(nlp, :neval_obj)
   mul!(nlp.v, nlp.J, x)
   nlp.v .+= nlp.F
+  skip_sigma && return dot(nlp.v, nlp.v)/2
   return (dot(nlp.v, nlp.v) + nlp.σ * dot(x, x)) / 2
 end
 

src/LM_alg.jl

@@ -300,7 +300,7 @@ function SolverCore.solve!(
   end
 
   mk = let ψ = ψ, solver = solver
-    d -> obj(solver.subpb.model, d) + ψ(d)
+    d -> obj(solver.subpb.model, d, skip_sigma = true) + ψ(d)
   end
 
   prox!(s, ψ, mν∇fk, ν)

src/R2N.jl

@@ -327,7 +327,7 @@ function SolverCore.solve!(
   end
 
   mk = let ψ = ψ, solver = solver
-    d -> obj(solver.subpb.model, d) + ψ(d)::T
+    d -> obj(solver.subpb.model, d, skip_sigma = true) + ψ(d)::T
   end
 
   prox!(s1, ψ, mν∇fk, ν₁)

src/R2NModel.jl

@@ -34,10 +34,11 @@ function R2NModel(B::G, ∇f::V, σ::T, x0::V) where {T, V, G}
   return R2NModel(B::G, ∇f::V, v::V, σ::T, meta, Counters())
 end
 
-function NLPModels.obj(nlp::R2NModel, x::AbstractVector)
+function NLPModels.obj(nlp::R2NModel, x::AbstractVector; skip_sigma = false)
   @lencheck nlp.meta.nvar x
   increment!(nlp, :neval_obj)
   mul!(nlp.v, nlp.B, x)
+  skip_sigma && return dot(nlp.v, x)/2 + dot(nlp.∇f, x)
   return dot(nlp.v, x)/2 + dot(nlp.∇f, x) + nlp.σ * dot(x, x) / 2
 end