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fix(notebook-python,#14532): restaure la section Applications reelles du TSP (App-13) #14811

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Merged
myia-ai-01 merged 4 commits into from
Sep 6, 2026
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fix(notebook-python,#14532): restaure la section Applications reelles du TSP (App-13) #14811

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ad81b62
fix(notebook-python): restaure la section Applications reelles du TSP
jsboige Sep 5, 2026
c14a221
chore(twin-parity): re-atteste la paire App-13 apres restauration App…
jsboige Sep 5, 2026
00c60da
Merge branch 'main' into fix/14532-app13-applications-reelles
jsboige Sep 5, 2026
5625526
Merge branch 'main' into fix/14532-app13-applications-reelles
jsboige Sep 6, 2026
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13 changes: 11 additions & 2 deletions MyIA.AI.Notebooks/Search/Applications/Hybrid/App-13-TSP-Metaheuristics.ipynb
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Expand Up @@ -66,12 +66,21 @@
"3. **Richesse des methodes** : de la force brute aux algorithmes evolutionnaires, le TSP se pretent a une comparaison methodologique structuree (cf. tableau synthese du notebook).\n",
"4. **Application industrielle** : logistique (livraisons, tournes), circuits electroniques (VLSI), ordre de visitation en robotique.\n",
"\n",
"### Applications reelles\n",
"\n",
"Le TSP modelise des problemes d'optimisation concrets :\n",
"\n",
"- **Logistique** : Tournees de livraison, collecte de dechets.\n",
"- **Fabrication** : Percage de circuits imprimes, decoupe laser.\n",
"- **Genomique** : Assemblage de sequences ADN.\n",
"- **Astronomie** : Planification d'observations telescopiques.\n",
"\n",
"**Trois variantes canoniques** :\n",
"- **TSP symetrique** : `d(i, j) = d(j, i)` (cas par defaut du notebook).\n",
"- **TSP asymetrique (ATSP)** : `d(i, j) != d(j, i)` (cf. Bonus 1, code[16]).\n",
"- **TSP avec fenetre horaire (TSPTW)** : chaque ville doit etre visitee dans une fenetre [a_i, b_i] (extension classique).\n",
"\n",
"**Note d'ancrage sur la suite du notebook** : le notebook utilise `TSPInstance.generer_aleatoire(n, graine)` pour creer des instances aleatoires reproductibles. La matrice de distances est symetrique par defaut, calculee comme `d(i, j) = sqrt((x_i - x_j)^2 + (y_i - y_j)^2)` entre coordonnees 2D."
"**Note d'ancrage sur la suite du notebook** : le notebook utilise `TSPInstance.generer_aleatoire(n, graine)` pour creer des instances aleatoires reproductibles. La matrice de distances est symetrique par defaut, calculee comme `d(i, j) = sqrt((x_i - x_j)^2 + (y_i - y_j)^2)` entre coordonnees 2D.\n"
]
},
{
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},
"nbformat": 4,
"nbformat_minor": 5
}
}
6 changes: 6 additions & 0 deletions scripts/notebook_tools/twin_pairs.d/app-13-tsp-metaheuristics.yaml
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Expand Up @@ -45,6 +45,12 @@
csharp_sha: d8d5171046c954064398c37fa90dc4ab89b0a70e
content_python_sha: a55722e21d69984106cdc08dcf56befb39409d74e348ff3ee62d8c94758eb736
content_csharp_sha: bbc531e701c1413689a7d6e1ceb87d0eb0a70e056622f0384b31e0970d9c09ce
- date: "2026-09-05"
by: myia-po-2024:CoursIA-2
python_sha: dd4a71af2fd0dfdd8052f118286c9d6441a67622
csharp_sha: d8d5171046c954064398c37fa90dc4ab89b0a70e
content_python_sha: 4c5c8a247af6f9a23385f9c3748dcd6adac0a522a411893d17abacb9ef90a9da
content_csharp_sha: bbc531e701c1413689a7d6e1ceb87d0eb0a70e056622f0384b31e0970d9c09ce
bridge_verdict: SOTA-OK
bridge_verdict_reason: "Python = OR-Tools constraint_solver routing (pywrapcp.RoutingModel + GLS Guided Local Search, OR-Tools 9.11) + numpy + matplotlib + itertools (22 cellules code). C# = Google.OrTools.ConstraintSolver (NuGet, C# binding du MEME engine OR-Tools 9.11) + Tranche 1 from-scratch BCL pure (System.*, 0 NuGet) PRESERVEE (11 cellules code). Verdict SOTA-OK (lib-vs-lib native-both) : les deux cotes invoquent le MEME moteur OR-Tools routing 9.11 (pywrapcp.RoutingModel <-> Google.OrTools.ConstraintSolver.RoutingModel). Prong-B (#3801) : discrimination metaheuristique vs exact (GLS ameliore significativement les couts vs nearest-neighbor from-scratch). Tranche 1 from-scratch preservee intacte (byte-identique a main). Python met l'accent sur SOTA + viz matplotlib + experimentation comparative ; C# demontre from-scratch + binding NuGet. parity_level: native-both preserve."
known_differences:
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