add reduce_TR doc

Author: MaxenceGollier

src/TRDH_alg.jl

@@ -134,11 +134,12 @@ For advanced usage, first define a solver "TRDHSolver" to preallocate the memory
 - `η1::T = √√eps(T)`: successful iteration threshold;
 - `η2::T = T(0.9)`: very successful iteration threshold;
 - `γ::T = T(3)`: trust-region radius parameter multiplier. Must satisfy `γ > 1`. The trust-region radius is updated as Δ := Δ*γ when the iteration is very successful and Δ := Δ/γ when the iteration is unsuccessful;
-- `reduce_TR::Bool = True`: TODO
+- `reduce_TR::Bool = true`: see explanation on the stopping criterion below;
 - `χ::F =  NormLinf(1)`: norm used to define the trust-region;`
 - `D::L = nothing`: diagonal quasi-Newton approximation used for the model φ. If nothing is provided and `reg_nlp.model` is not a diagonal quasi-Newton approximation, a spectral gradient approximation is used.`
 
 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.
+Alternatively, if `reduce_TR = true`, then ξₖ₁ := f(xₖ) + h(xₖ) - φ(sₖ₁; xₖ) - ψ(sₖ₁; xₖ) is used instead of ξₖ, where sₖ₁ is the Cauchy point.
 
 #  Output
 The value returned is a `GenericExecutionStats`, see `SolverCore.jl`.