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Implémentation des fonctions de chaque question
dpo
reviewed
Aug 18, 2025
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dpo
reviewed
Aug 19, 2025
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| t *= beta | ||
| if t < 1e-10 | ||
| return t, xt |
| x_prev .= x | ||
| t, x = armijo(f, gradf, x, C, alpha, beta) | ||
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| diff = norm(x - x_prev, Inf) |
Contributor
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La mesure de stationnarité n'est pas la longueur du pas...
| println("Itérations: ", result.iterations) | ||
| println("Solution x*: ", round.(result.x, digits=5)) | ||
| end | ||
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Contributor
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Et la comparaison avec IPOPT ?
| end | ||
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| return x | ||
| end |
| function quadratic_example() | ||
| n = 10 | ||
| Q = randn(n, n) | ||
| Q = Q' * Q |
| Traditionnellement, les problèmes de région de confiance sont résolus par des méthodes comme le **gradient conjugué tronqué**, qui utilisent la structure quadratique. | ||
| Cependant, notre approche par le **gradient projeté** offre plusieurs avantages : | ||
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| - Contrairement au GCT, elle ne nécessite pas que \( B_k \) soit définie positive. |
| Cependant, notre approche par le **gradient projeté** offre plusieurs avantages : | ||
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| - Contrairement au GCT, elle ne nécessite pas que \( B_k \) soit définie positive. | ||
| - La solution x* satisfait toujours \( \| x^{*} \| \leq \Delta \). |
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Implémentation des fonctions de chaque question