remove old doc

Author: MaxenceGollier

src/LMTR_alg.jl

@@ -510,44 +510,4 @@ function SolverCore.solve!(
   set_solution!(stats, xk)
   set_residuals!(stats, zero(T), sqrt_ξ1_νInv)
   return stats
-end
-
-"""
-    LMTR(nls, h, χ, options; kwargs...)
-
-A trust-region Levenberg-Marquardt method for the problem
-
-    min ½ ‖F(x)‖² + h(x)
-
-where F: ℝⁿ → ℝᵐ and its Jacobian J are Lipschitz continuous and h: ℝⁿ → ℝ is
-lower semi-continuous and proper.
-
-At each iteration, a step s is computed as an approximate solution of
-
-    min  ½ ‖J(x) s + F(x)‖₂² + ψ(s; x)  subject to  ‖s‖ ≤ Δ
-
-where F(x) and J(x) are the residual and its Jacobian at x, respectively, ψ(s; x) = h(x + s),
-‖⋅‖ is a user-defined norm and Δ > 0 is a trust-region radius.
-
-### Arguments
-
-* `nls::AbstractNLSModel`: a smooth nonlinear least-squares problem
-* `h`: a regularizer such as those defined in ProximalOperators
-* `χ`: a norm used to define the trust region in the form of a regularizer
-* `options::ROSolverOptions`: a structure containing algorithmic parameters
-
-### Keyword arguments
-
-* `x0::AbstractVector`: an initial guess (default: `nls.meta.x0`)
-* `subsolver_logger::AbstractLogger`: a logger to pass to the subproblem solver
-* `subsolver`: the procedure used to compute a step (`PG`, `R2` or `TRDH`)
-* `subsolver_options::ROSolverOptions`: default options to pass to the subsolver.
-* `selected::AbstractVector{<:Integer}`: (default `1:nls.meta.nvar`).
-
-### Return values
-
-* `xk`: the final iterate
-* `Fobj_hist`: an array with the history of values of the smooth objective
-* `Hobj_hist`: an array with the history of values of the nonsmooth objective
-* `Complex_hist`: an array with the history of number of inner iterations.
-"""
+end