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Aug 17, 2025
| f(x) + 0.5*ρ*‖c(x)‖², avec gradient et opérateur hessien augmentés. | ||
| """ | ||
| function quad_penalty_adnlp(nlp::ADNLPModel, ρ::Real) | ||
| pure_c(x) = (out = Vector{eltype(x)}(undef, nlp.meta.ncon); |
| # Objectif pénalisé | ||
| fquad(x) = obj(nlp, x) + 0.5 * ρ * norm(pure_c(x))^2 | ||
| # Gradient pénalisé | ||
| gradquad(x) = grad(nlp, x) + ρ * (pure_jac(x)' * pure_c(x)) |
Contributor
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Tu évalues les contraintes deux fois.
| gradquad(x) = grad(nlp, x) + ρ * (pure_jac(x)' * pure_c(x)) | ||
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| # Opérateur Hessien (matrix‑free) | ||
| function hessopquad(x) |
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Tu aurais pu écrire Hop + Jop' * Jop avec Jop = jac_op(nlp, x).
| v -> Hop*v + ρ * (J' * (J*v)), | ||
| v -> Hop'*v + ρ * (J' * (J*v))) | ||
| end | ||
| # Hessien dense pour Newton modifiée (application de l'opérateur) |
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??? Newton modifiée n'a pas besoin d'une matrice dense, mais creuse.
| statut = string(stats.status) | ||
| )) | ||
| end | ||
| end |
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Ton rapport fait 538 pages ...
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