Apply suggestions from code review

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Author: MaxenceGollier

src/LMTR_alg.jl

@@ -146,7 +146,7 @@ For advanced usage, first define a solver "LMSolver" to preallocate the memory u
 - `subsolver::S = R2Solver`: subsolver used to solve the subproblem that appears at each iteration.
 - `sub_kwargs::NamedTuple = NamedTuple()`: a named tuple containing the keyword arguments to be sent to the subsolver. The solver will fail if invalid keyword arguments are provided to the subsolver. For example, if the subsolver is `R2Solver`, you can pass `sub_kwargs = (max_iter = 100, σmin = 1e-6,)`.
 
-The algorithm stops either when `√(ξₖ/νₖ) < atol + rtol*√(ξ₀/ν₀) ` or `ξₖ < 0` and `√(-ξₖ/νₖ) < neg_tol` where ξₖ := f(xₖ) + h(xₖ) - φ(sₖ; xₖ) - ψ(sₖ; xₖ), and √(ξₖ/νₖ) is a stationarity measure or when `‖sₖ₁‖/νₖ < atol_step + rtol_step*‖s₀‖/ν₀` where `sₖ₁` is the Cauchy step.
+The algorithm stops either when `√(ξₖ/νₖ) < atol_decr + rtol_decr*√(ξ₀/ν₀) ` or `ξₖ < 0` and `√(-ξₖ/νₖ) < neg_tol` where ξₖ := f(xₖ) + h(xₖ) - φ(sₖ; xₖ) - ψ(sₖ; xₖ), and √(ξₖ/νₖ) is a stationarity measure or when `‖sₖ₁‖/νₖ < atol_step + rtol_step*‖s₀‖/ν₀` where `sₖ₁` is the Cauchy step.
 
 # Output
 The value returned is a `GenericExecutionStats`, see `SolverCore.jl`.
@@ -325,7 +325,7 @@ function SolverCore.solve!(
   atol_step += rtol_step * norm_s_cauchydν # make stopping test absolute and relative
 
   solved = (ξ1 < 0 && sqrt_ξ1_νInv ≤ neg_tol) || (ξ1 ≥ 0 && sqrt_ξ1_νInv ≤ atol_decr) || (norm_s_cauchydν ≤ atol_step)
-  
+
   set_status!(
     stats,
     get_status(
@@ -478,7 +478,7 @@ function SolverCore.solve!(
 
     norm_s_cauchy = norm(s)
     norm_s_cauchydν = norm_s_cauchy / ν
-    
+
     solved = (ξ1 < 0 && sqrt_ξ1_νInv ≤ neg_tol) || (ξ1 ≥ 0 && sqrt_ξ1_νInv ≤ atol_decr) || (norm_s_cauchydν ≤ atol_step)
 
     set_status!(

src/LM_alg.jl

@@ -146,7 +146,7 @@ For advanced usage, first define a solver "LMSolver" to preallocate the memory u
 - `subsolver = R2Solver`: the solver used to solve the subproblems.
 - `sub_kwargs::NamedTuple = NamedTuple()`: a named tuple containing the keyword arguments to be sent to the subsolver. The solver will fail if invalid keyword arguments are provided to the subsolver. For example, if the subsolver is `R2Solver`, you can pass `sub_kwargs = (max_iter = 100, σmin = 1e-6,)`.
 
-The algorithm stops either when `√(ξₖ/νₖ) < atol + rtol*√(ξ₀/ν₀) ` or `ξₖ < 0` and `√(-ξₖ/νₖ) < neg_tol` where ξₖ := f(xₖ) + h(xₖ) - φ(sₖ; xₖ) - ψ(sₖ; xₖ), and √(ξₖ/νₖ) is a stationarity measure or when `‖sₖ₁‖/νₖ < atol_step + rtol_step*‖s₀₁‖/ν₀` where `sₖ₁` is the Cauchy step.
+The algorithm stops either when `√(ξₖ/νₖ) < atol_decr + rtol_decr*√(ξ₀/ν₀) ` or `ξₖ < 0` and `√(-ξₖ/νₖ) < neg_tol` where ξₖ := f(xₖ) + h(xₖ) - φ(sₖ; xₖ) - ψ(sₖ; xₖ), and √(ξₖ/νₖ) is a stationarity measure or when `‖sₖ₁‖/νₖ < atol_step + rtol_step*‖s₀₁‖/ν₀` where `sₖ₁` is the Cauchy step.
 
 # Output
 The value returned is a `GenericExecutionStats`, see `SolverCore.jl`.

src/R2DH.jl

@@ -354,7 +354,7 @@ function SolverCore.solve!(
   norm_s = norm(s)
   norm_sdν = norm_s / ν₁
   atol_step += rtol_step * norm_sdν # make stopping test absolute and relative
-  
+
   solved = (ξ < 0 && sqrt_ξ_νInv ≤ neg_tol) || (ξ ≥ 0 && sqrt_ξ_νInv ≤ atol_decr) || (norm_sdν ≤ atol_step)
 
   set_status!(
@@ -452,7 +452,7 @@ function SolverCore.solve!(
     sqrt_ξ_νInv = ξ ≥ 0 ? sqrt(ξ / ν₁) : sqrt(-ξ / ν₁)
     (ξ < 0 && sqrt_ξ_νInv > neg_tol) &&
       error("R2DH: prox-gradient step should produce a decrease but ξ = $(ξ)")
-    
+
     norm_s = norm(s)
     norm_sdν = norm_s / ν₁
 

src/R2_alg.jl

@@ -160,7 +160,7 @@ For advanced usage, first define a solver "R2Solver" to preallocate the memory u
 - `compute_obj::Bool = true`: (advanced) whether `f(x₀)` should be computed or not. If set to false, then the value is retrieved from `stats.solver_specific[:smooth_obj]`;
 - `compute_grad::Bool = true`: (advanced) whether `∇f(x₀)` should be computed or not. If set to false, then the value is retrieved from `solver.∇fk`;
 
-The algorithm stops either when `√(ξₖ/νₖ) < atol + rtol*√(ξ₀/ν₀) ` or `ξₖ < 0` and `√(-ξₖ/νₖ) < neg_tol` where ξₖ := f(xₖ) + h(xₖ) - φ(sₖ; xₖ) - ψ(sₖ; xₖ), and √(ξₖ/νₖ) is a stationarity measure or when `‖sₖ‖/νₖ < atol_step + rtol_step*‖s₀‖/ν₀` where `sₖ` is the current step.
+The algorithm stops either when `√(ξₖ/νₖ) < atol_decr + rtol_decr*√(ξ₀/ν₀) ` or `ξₖ < 0` and `√(-ξₖ/νₖ) < neg_tol` where ξₖ := f(xₖ) + h(xₖ) - φ(sₖ; xₖ) - ψ(sₖ; xₖ), and √(ξₖ/νₖ) is a stationarity measure or when `‖sₖ‖/νₖ < atol_step + rtol_step*‖s₀‖/ν₀` where `sₖ` is the current step.
 
 # Output
 The value returned is a `GenericExecutionStats`, see `SolverCore.jl`.
@@ -429,7 +429,7 @@ function SolverCore.solve!(
   atol_step += rtol_step * norm_sdν # make stopping test absolute and relative
 
   solved = (ξ < 0 && sqrt_ξ_νInv ≤ neg_tol) || (ξ ≥ 0 && sqrt_ξ_νInv ≤ atol_decr) || (norm_sdν ≤ atol_step)
-  
+
   set_status!(
     stats,
     get_status(
@@ -518,7 +518,7 @@ function SolverCore.solve!(
     norm_sdν = norm_s / ν
 
     solved = (ξ < 0 && sqrt_ξ_νInv ≤ neg_tol) || (ξ ≥ 0 && sqrt_ξ_νInv ≤ atol_decr) || (norm_sdν ≤ atol_step)
-    
+
     set_status!(
       stats,
       get_status(

src/TRDH_alg.jl

@@ -405,7 +405,7 @@ function SolverCore.solve!(
   norm_sdν = norm_s / ν
   atol_step += rtol_step * norm_sdν # make stopping test absolute and relative
 
-  solved = (ξ1 < 0 && sqrt_ξ_νInv ≤ neg_tol) || (ξ1 ≥ 0 && sqrt_ξ_νInv ≤ atol_decr) | (norm_sdν ≤ atol_step)
+  solved = (ξ1 < 0 && sqrt_ξ_νInv ≤ neg_tol) || (ξ1 ≥ 0 && sqrt_ξ_νInv ≤ atol_decr) || (norm_sdν ≤ atol_step)
   set_status!(
     stats,
     get_status(

src/TR_alg.jl

@@ -355,7 +355,7 @@ function SolverCore.solve!(
   atol_step += rtol_step * norm_s_cauchydν # make stopping test absolute and relative
 
   solved = (ξ1 < 0 && sqrt_ξ1_νInv ≤ neg_tol) || (ξ1 ≥ 0 && sqrt_ξ1_νInv ≤ atol_decr) || (norm_s_cauchydν ≤ atol_step)
-  
+
   set_status!(
     stats,
     get_status(