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Add sigma_cauchy and scp_norm to solver statistics in all solvers #253

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Add sigma_cauchy and scp_norm to solver statistics in all solvers #253

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6026bf3
Add sigma_cauchy and scp_norm to solver statistics in LMTR, LMSolver,…
MohamedLaghdafHABIBOULLAH Oct 15, 2025
850248d
Add scp_norm to solver-specific entries in RegularizedExecutionStats
MohamedLaghdafHABIBOULLAH Oct 21, 2025
1ad2e75
Update .gitignore
MohamedLaghdafHABIBOULLAH Dec 6, 2025
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1 change: 1 addition & 0 deletions .gitignore
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@@ -0,0 +1 @@
**/Manifest.toml
4 changes: 4 additions & 0 deletions src/LMTR_alg.jl
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Expand Up @@ -285,6 +285,7 @@ function SolverCore.solve!(
set_objective!(stats, fk + hk)
set_solver_specific!(stats, :smooth_obj, fk)
set_solver_specific!(stats, :nonsmooth_obj, hk)
set_solver_specific!(stats, :sigma_cauchy, 1/ν)
set_solver_specific!(stats, :prox_evals, prox_evals + 1)

φ1 = let Fk = Fk, ∇fk = ∇fk
Expand All @@ -302,6 +303,7 @@ function SolverCore.solve!(
# Take first proximal gradient step s1 and see if current xk is nearly stationary.
# s1 minimizes φ1(d) + ‖d‖² / 2 / ν + ψ(d) ⟺ s1 ∈ prox{νψ}(-ν∇φ1(0))
prox!(s, ψ, mν∇fk, ν)
set_solver_specific!(stats, :scp_norm, norm(s))
ξ1 = fk + hk - mk1(s) + max(1, abs(fk + hk)) * 10 * eps()
sqrt_ξ1_νInv = ξ1 ≥ 0 ? sqrt(ξ1 / ν) : sqrt(-ξ1 / ν)
solved = (ξ1 < 0 && sqrt_ξ1_νInv ≤ neg_tol) || (ξ1 ≥ 0 && sqrt_ξ1_νInv ≤ atol)
Expand Down Expand Up @@ -448,9 +450,11 @@ function SolverCore.solve!(
set_solver_specific!(stats, :prox_evals, prox_evals + 1)

ν = α * Δk / (1 + σmax^2 * (α * Δk + 1))
set_solver_specific!(stats, :sigma_cauchy, 1/ν)
@. mν∇fk = -∇fk * ν

prox!(s, ψ, mν∇fk, ν)
set_solver_specific!(stats, :scp_norm, norm(s))
mks = mk1(s)

ξ1 = fk + hk - mks + max(1, abs(hk)) * 10 * eps()
Expand Down
4 changes: 4 additions & 0 deletions src/LM_alg.jl
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Expand Up @@ -290,6 +290,7 @@ function SolverCore.solve!(
set_objective!(stats, fk + hk)
set_solver_specific!(stats, :smooth_obj, fk)
set_solver_specific!(stats, :nonsmooth_obj, hk)
set_solver_specific!(stats, :sigma_cauchy, 1/ν)
set_solver_specific!(stats, :prox_evals, prox_evals + 1)
m_monotone > 1 && (m_fh_hist[stats.iter % (m_monotone - 1) + 1] = fk + hk)

Expand All @@ -306,6 +307,7 @@ function SolverCore.solve!(
end

prox!(s, ψ, mν∇fk, ν)
set_solver_specific!(stats, :scp_norm, norm(s))
ξ1 = fk + hk - mk1(s) + max(1, abs(fk + hk)) * 10 * eps()
sqrt_ξ1_νInv = ξ1 ≥ 0 ? sqrt(ξ1 / ν) : sqrt(-ξ1 / ν)
solved = (ξ1 < 0 && sqrt_ξ1_νInv ≤ neg_tol) || (ξ1 ≥ 0 && sqrt_ξ1_νInv ≤ atol)
Expand Down Expand Up @@ -436,9 +438,11 @@ function SolverCore.solve!(
set_solver_specific!(stats, :prox_evals, prox_evals + 1)

ν = θ / (σmax^2 + σk) # ‖J'J + σₖ I‖ = ‖J‖² + σₖ
set_solver_specific!(stats, :sigma_cauchy, 1/ν)

@. mν∇fk = - ν * ∇fk
prox!(s, ψ, mν∇fk, ν)
set_solver_specific!(stats, :scp_norm, norm(s))
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@dpo dpo Dec 2, 2025

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This norm is already computed. Don't compute it twice.

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@MohamedLaghdafHABIBOULLAH MohamedLaghdafHABIBOULLAH Dec 6, 2025 •
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Here, $$s$$ actually refers to $$s_{cp}$$, whereas the other norm being computed corresponds to the step $$s_{k}$$ used in the update $$x_{k+1} = x_{k} + s_{k}.$$

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@MohamedLaghdafHABIBOULLAH MohamedLaghdafHABIBOULLAH Dec 6, 2025

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I see the issue when we consider R2, R2DH or TRDH

mks = mk1(s)

ξ1 = fk + hk - mks + max(1, abs(hk)) * 10 * eps()
Expand Down
4 changes: 3 additions & 1 deletion src/R2DH.jl
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Expand Up @@ -328,6 +328,7 @@ function SolverCore.solve!(
mk(d)::T = φ(d) + ψ(d)::T

spectral_test ? prox!(s, ψ, mν∇fk, ν₁) : iprox!(s, ψ, ∇fk, dkσk)
set_solver_specific!(stats, :scp_norm, norm(s))

mks = mk(s)

Expand Down Expand Up @@ -412,19 +413,20 @@ function SolverCore.solve!(
set_solver_specific!(stats, :smooth_obj, fk)
set_solver_specific!(stats, :nonsmooth_obj, hk)
set_solver_specific!(stats, :sigma, σk)
set_solver_specific!(stats, :sigma_cauchy, 1/ν₁)
set_iter!(stats, stats.iter + 1)
set_time!(stats, time() - start_time)

@. dkσk = D.d .+ σk
DNorm = norm(D.d, Inf)

ν₁ = θ / (DNorm + σk)
set_solver_specific!(stats, :sigma_cauchy, 1/ν₁)

@. mν∇fk = -ν₁ * ∇fk
m_monotone > 1 && (m_fh_hist[stats.iter % (m_monotone - 1) + 1] = fk + hk)

spectral_test ? prox!(s, ψ, mν∇fk, ν₁) : iprox!(s, ψ, ∇fk, dkσk)
set_solver_specific!(stats, :scp_norm, norm(s))
mks = mk(s)

ξ = hk - mks + max(1, abs(hk)) * 10 * eps()
Expand Down
2 changes: 2 additions & 0 deletions src/R2N.jl
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Expand Up @@ -331,6 +331,7 @@ function SolverCore.solve!(
end

prox!(s1, ψ, mν∇fk, ν₁)
set_solver_specific!(stats, :scp_norm, norm(s1))
mks = mk1(s1)

ξ1 = hk - mks + max(1, abs(hk)) * 10 * eps()
Expand Down Expand Up @@ -477,6 +478,7 @@ function SolverCore.solve!(

@. mν∇fk = - ν₁ * ∇fk
prox!(s1, ψ, mν∇fk, ν₁)
set_solver_specific!(stats, :scp_norm, norm(s1))
mks = mk1(s1)

ξ1 = hk - mks + max(1, abs(hk)) * 10 * eps()
Expand Down
2 changes: 2 additions & 0 deletions src/R2_alg.jl
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Expand Up @@ -402,6 +402,7 @@ function SolverCore.solve!(
mk(d)::T = φk(d) + ψ(d)::T

prox!(s, ψ, mν∇fk, ν)
set_solver_specific!(stats, :scp_norm, norm(s))
mks = mk(s)

ξ = hk - mks + max(1, abs(hk)) * 10 * eps()
Expand Down Expand Up @@ -489,6 +490,7 @@ function SolverCore.solve!(
set_time!(stats, time() - start_time)

prox!(s, ψ, mν∇fk, ν)
set_solver_specific!(stats, :scp_norm, norm(s))
mks = mk(s)

ξ = hk - mks + max(1, abs(hk)) * 10 * eps()
Expand Down
6 changes: 6 additions & 0 deletions src/TRDH_alg.jl
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Expand Up @@ -333,6 +333,7 @@ function SolverCore.solve!(
set_objective!(stats, fk + hk)
set_solver_specific!(stats, :smooth_obj, fk)
set_solver_specific!(stats, :nonsmooth_obj, hk)
set_solver_specific!(stats, :sigma_cauchy, 1/ν)

# models
φ1 = let ∇fk = ∇fk
Expand All @@ -358,6 +359,7 @@ function SolverCore.solve!(

if reduce_TR
prox!(s, ψ, mν∇fk, ν)
set_solver_specific!(stats, :scp_norm, norm(s))
mks = mk1(s)

ξ1 = hk - mks + max(1, abs(hk)) * 10 * eps()
Expand All @@ -383,6 +385,7 @@ function SolverCore.solve!(
sNorm = χ(s)

if !reduce_TR
set_solver_specific!(stats, :scp_norm, norm(s))
sqrt_ξ_νInv = ξ ≥ 0 ? sqrt(ξ / ν) : sqrt(-ξ / ν)
solved = (ξ < 0 && sqrt_ξ_νInv ≤ neg_tol) || (ξ ≥ 0 && sqrt_ξ_νInv < atol)
(ξ < 0 && sqrt_ξ_νInv > neg_tol) &&
Expand Down Expand Up @@ -473,10 +476,12 @@ function SolverCore.solve!(
set_time!(stats, time() - start_time)

ν = reduce_TR ? (α * Δk)/(DNorm + one(T)) : α / (DNorm + one(T))
set_solver_specific!(stats, :sigma_cauchy, 1/ν)
mν∇fk .= -ν .* ∇fk

if reduce_TR
prox!(s, ψ, mν∇fk, ν)
set_solver_specific!(stats, :scp_norm, norm(s))
ξ1 = hk - mk1(s) + max(1, abs(hk)) * 10 * eps()
sqrt_ξ_νInv = ξ1 ≥ 0 ? sqrt(ξ1 / ν) : sqrt(-ξ1 / ν)
solved = (ξ1 < 0 && sqrt_ξ_νInv ≤ neg_tol) || (ξ1 ≥ 0 && sqrt_ξ_νInv < atol)
Expand All @@ -489,6 +494,7 @@ function SolverCore.solve!(
sNorm = χ(s)

if !reduce_TR
set_solver_specific!(stats, :scp_norm, norm(s))
ξ = hk - mk(s) + max(1, abs(hk)) * 10 * eps()
sqrt_ξ_νInv = ξ ≥ 0 ? sqrt(ξ / ν) : sqrt(-ξ / ν)
solved = (ξ < 0 && sqrt_ξ_νInv ≤ neg_tol) || (ξ ≥ 0 && sqrt_ξ_νInv < atol)
Expand Down
4 changes: 4 additions & 0 deletions src/TR_alg.jl
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Expand Up @@ -314,6 +314,7 @@ function SolverCore.solve!(
set_objective!(stats, fk + hk)
set_solver_specific!(stats, :smooth_obj, fk)
set_solver_specific!(stats, :nonsmooth_obj, hk)
set_solver_specific!(stats, :sigma_cauchy, 1/ν₁)
set_solver_specific!(stats, :prox_evals, prox_evals + 1)
m_monotone > 1 && (m_fh_hist[stats.iter % (m_monotone - 1) + 1] = fk + hk)

Expand All @@ -330,6 +331,7 @@ function SolverCore.solve!(
end

prox!(s, ψ, mν∇fk, ν₁)
set_solver_specific!(stats, :scp_norm, norm(s))
ξ1 = hk - mk1(s) + max(1, abs(hk)) * 10 * eps()
ξ1 > 0 || error("TR: first prox-gradient step should produce a decrease but ξ1 = $(ξ1)")
sqrt_ξ1_νInv = sqrt(ξ1 / ν₁)
Expand Down Expand Up @@ -489,9 +491,11 @@ function SolverCore.solve!(
set_solver_specific!(stats, :prox_evals, prox_evals + 1)

ν₁ = α * Δk / (1 + λmax * (α * Δk + 1))
set_solver_specific!(stats, :sigma_cauchy, 1/ν₁)
@. mν∇fk = -ν₁ * ∇fk

prox!(s, ψ, mν∇fk, ν₁)
set_solver_specific!(stats, :scp_norm, norm(s))
ξ1 = hk - mk1(s) + max(1, abs(hk)) * 10 * eps()
sqrt_ξ1_νInv = sqrt(ξ1 / ν₁)

Expand Down
1 change: 1 addition & 0 deletions src/utils.jl
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Expand Up @@ -128,6 +128,7 @@ function RegularizedExecutionStats(reg_nlp::AbstractRegularizedNLPModel{T, V}) w
set_solver_specific!(stats, :sigma_cauchy, T(Inf))
set_solver_specific!(stats, :radius, T(Inf))
set_solver_specific!(stats, :prox_evals, T(Inf))
set_solver_specific!(stats, :scp_norm, T(Inf))
return stats
end

Expand Down
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