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dpo
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Aug 17, 2025
| # votre code ici | ||
| # Package to do LDL factorization (required for the modified Newton implementation) | ||
| Pkg.add("LDLFactorizations") | ||
| using LDLFactorizations |
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Mets ces instructions dans la cellule au-dessus pour qu'on ne voie pas la sortie.
| # Source: https://www.cs.nthu.edu.tw/~cherung/teaching/2011cs5321/handout3.pdf (slide 8/20) | ||
| # Could be vectorized for improved performance. | ||
| for i in 1:size(LDL.D, 1) | ||
| LDL.D[i, i] = max(LDL.D[i, i], delta) |
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Ok, mais on a vu comment faire en labo.
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| # For stability purposes -> normalize the descent direction if large | ||
| dk_norm = norm(dk) | ||
| if dk_norm > 1.0e2 |
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Là, tu détruis la direction de Newton car un pas t = 1 le long de la direction normalisée n'est plus un pas de Newton...
| alphak = 1.0 | ||
| fk = obj(model, xk) | ||
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| for i in 1:50 |
Contributor
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et si on atteint i = 50, que se passe-t-il ?
| Hk .= hess(model, xk) | ||
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| # For local superlinear convergence (Nocedal, page 168) | ||
| eta_k = min(0.5, sqrt(norm_gk)) |
| eta_k = min(0.5, sqrt(norm_gk)) | ||
| eps_k = eta_k*norm_gk | ||
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| zj = zeros(dim) |
Contributor
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On ne demande PAS d'implémenter CG ...
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