diff --git a/MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03-Informed-CSharp.ipynb b/MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03-Informed-CSharp.ipynb
index 8ea8bf4ebe..492fb89e7e 100644
--- a/MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03-Informed-CSharp.ipynb
+++ b/MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03-Informed-CSharp.ipynb
@@ -129,7 +129,6 @@
        "        The below script needs to be able to find the current output cell; this is an easy method to get it.\r\n",
        "    </div>\r\n",
        "    <script type='text/javascript'>\r\n",
-       
        "    function timeout(ms, promise) {\r\n",
        "        return new Promise(function (resolve, reject) {\r\n",
        "            setTimeout(function () {\r\n",
@@ -139,10 +138,7 @@
        "        })\r\n",
        "    }\r\n",
        "\r\n",
-       
-       
        "\r\n",
-       
        "\r\n",
        "            if (!rootUrl.endsWith('/')) {\r\n",
        "                rootUrl = `${rootUrl}/`;\r\n",
@@ -157,7 +153,6 @@
        "                    headers: {\r\n",
        "                        'Content-Type': 'text/plain'\r\n",
        "                    },\r\n",
-       
        "                }));\r\n",
        "\r\n",
        "                if (response.status == 200) {\r\n",
@@ -169,8 +164,6 @@
        "    }\r\n",
        "}\r\n",
        "\r\n",
-       
-       
        "        .then((root) => {\r\n",
        "        // use probing to find host url and api resources\r\n",
        "        // load interactive helpers and language services\r\n",
@@ -219,13 +212,11 @@
        "    \r\n",
        "    \r\n",
        "    require_script.onload = function() {\r\n",
-       
        "    };\r\n",
        "\r\n",
        "    document.getElementsByTagName('head')[0].appendChild(require_script);\r\n",
        "}\r\n",
        "else {\r\n",
-       
        "}\r\n",
        "\r\n",
        "    </script>\r\n",
@@ -2002,7 +1993,10 @@
     "    var exploredOrder = new List<string>();\n",
     "\n",
     "    // DFS recursif : retourne (solution, prochaine borne).\n",
-    "    (Node sol, double nextBound) Dfs(Node node, double curBound) {\n",
+    "    // depth = profondeur recursive (= memoire de pile reelle d'IDA*).\n",
+    "    int maxDepth = 0;\n",
+    "    (Node sol, double nextBound) Dfs(Node node, double curBound, int depth) {\n",
+    "        if (depth > maxDepth) maxDepth = depth;\n",
     "        double f = node.PathCost + h(node.State);\n",
     "        if (f > curBound) return (null, f);             // coupure\n",
     "        if (problem.GoalTest(node.State)) return (node, curBound);\n",
@@ -2011,7 +2005,7 @@
     "        double minNext = double.PositiveInfinity;\n",
     "        foreach (var child in node.Expand(problem)) {\n",
     "            totalGenerated++;\n",
-    "            var (sol, childBound) = Dfs(child, curBound);\n",
+    "            var (sol, childBound) = Dfs(child, curBound, depth + 1);\n",
     "            if (sol != null) return (sol, curBound);\n",
     "            if (childBound < minNext) minNext = childBound;\n",
     "        }\n",
@@ -2023,16 +2017,18 @@
     "    while (iterations < maxIters) {\n",
     "        iterations++;\n",
     "        if (verbose) Console.WriteLine($\"  Iteration {iterations}: f-limit = {bound:F0}\");\n",
-    "        var (result, newBound) = Dfs(root, bound);\n",
+    "        var (result, newBound) = Dfs(root, bound, 0);\n",
     "        if (result != null) {\n",
     "            sw.Stop();\n",
-    "            return new SearchResult(\"IDA*\", result, totalExpanded, totalGenerated, iterations, sw.Elapsed.TotalMilliseconds, exploredOrder);\n",
+    "            // 5eme slot = max_frontier_size. Pour IDA* la frontiere n'existe pas,\n",
+    "        // on y passe la profondeur recursive max (= memoire de pile reelle, O(bd)).\n",
+    "        return new SearchResult(\"IDA*\", result, totalExpanded, totalGenerated, maxDepth, sw.Elapsed.TotalMilliseconds, exploredOrder);\n",
     "        }\n",
     "        if (double.IsInfinity(newBound)) break;  // espace epuise, pas de solution\n",
     "        bound = newBound;\n",
     "    }\n",
     "    sw.Stop();\n",
-    "    return new SearchResult(\"IDA*\", null, totalExpanded, totalGenerated, iterations, sw.Elapsed.TotalMilliseconds, exploredOrder);\n",
+    "    return new SearchResult(\"IDA*\", null, totalExpanded, totalGenerated, maxDepth, sw.Elapsed.TotalMilliseconds, exploredOrder);\n",
     "}\n",
     "\n",
     "Console.WriteLine(\"Algorithme IDA* implemente.\");"
diff --git a/MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03-Informed.ipynb b/MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03-Informed.ipynb
index 794968d219..18be94ff2a 100644
--- a/MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03-Informed.ipynb
+++ b/MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03-Informed.ipynb
@@ -5,10 +5,10 @@
    "id": "nav-header",
    "metadata": {
     "papermill": {
-     "duration": 0.013156,
-     "end_time": "2026-10-05T11:06:19.829819+00:00",
+     "duration": 0.008573,
+     "end_time": "2026-10-20T06:27:53.496192+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:19.816663+00:00",
+     "start_time": "2026-10-20T06:27:53.487619+00:00",
      "status": "completed"
     },
     "tags": []
@@ -40,16 +40,16 @@
    "id": "imports",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:19.854139Z",
-     "iopub.status.busy": "2026-10-05T11:06:19.853743Z",
-     "iopub.status.idle": "2026-10-05T11:06:21.933080Z",
-     "shell.execute_reply": "2026-10-05T11:06:21.930511Z"
+     "iopub.execute_input": "2026-10-20T06:27:53.513005Z",
+     "iopub.status.busy": "2026-10-20T06:27:53.512820Z",
+     "iopub.status.idle": "2026-10-20T06:27:55.939441Z",
+     "shell.execute_reply": "2026-10-20T06:27:55.938761Z"
     },
     "papermill": {
-     "duration": 2.094607,
-     "end_time": "2026-10-05T11:06:21.935755+00:00",
+     "duration": 2.441387,
+     "end_time": "2026-10-20T06:27:55.945243+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:19.841148+00:00",
+     "start_time": "2026-10-20T06:27:53.503856+00:00",
      "status": "completed"
     },
     "tags": []
@@ -95,10 +95,10 @@
    "id": "intro-section",
    "metadata": {
     "papermill": {
-     "duration": 0.012495,
-     "end_time": "2026-10-05T11:06:21.959841+00:00",
+     "duration": 0.006243,
+     "end_time": "2026-10-20T06:27:55.957968+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:21.947346+00:00",
+     "start_time": "2026-10-20T06:27:55.951725+00:00",
      "status": "completed"
     },
     "tags": []
@@ -147,10 +147,10 @@
    "id": "framework-section",
    "metadata": {
     "papermill": {
-     "duration": 0.012284,
-     "end_time": "2026-10-05T11:06:21.981711+00:00",
+     "duration": 0.0065,
+     "end_time": "2026-10-20T06:27:55.971459+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:21.969427+00:00",
+     "start_time": "2026-10-20T06:27:55.964959+00:00",
      "status": "completed"
     },
     "tags": []
@@ -179,16 +179,16 @@
    "id": "node-class",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:22.009107Z",
-     "iopub.status.busy": "2026-10-05T11:06:22.008561Z",
-     "iopub.status.idle": "2026-10-05T11:06:22.027536Z",
-     "shell.execute_reply": "2026-10-05T11:06:22.025811Z"
+     "iopub.execute_input": "2026-10-20T06:27:55.987619Z",
+     "iopub.status.busy": "2026-10-20T06:27:55.987110Z",
+     "iopub.status.idle": "2026-10-20T06:27:55.998712Z",
+     "shell.execute_reply": "2026-10-20T06:27:55.997781Z"
     },
     "papermill": {
-     "duration": 0.036832,
-     "end_time": "2026-10-05T11:06:22.029373+00:00",
+     "duration": 0.020878,
+     "end_time": "2026-10-20T06:27:55.999608+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:21.992541+00:00",
+     "start_time": "2026-10-20T06:27:55.978730+00:00",
      "status": "completed"
     },
     "tags": []
@@ -294,7 +294,8 @@
     "\n",
     "    def __init__(self, algorithm, solution_node=None, nodes_expanded=0,\n",
     "                 nodes_generated=0, max_frontier_size=0, elapsed_ms=0,\n",
-    "                 explored_order=None, heuristic_name=\"\"):\n",
+    "                 explored_order=None, heuristic_name=\"\",\n",
+    "                 iterations=0):\n",
     "        self.algorithm = algorithm\n",
     "        self.solution_node = solution_node\n",
     "        self.nodes_expanded = nodes_expanded\n",
@@ -303,6 +304,9 @@
     "        self.elapsed_ms = elapsed_ms\n",
     "        self.explored_order = explored_order or []\n",
     "        self.heuristic_name = heuristic_name\n",
+    "        # iterations : nombre de bornes f explorees (utilise par IDA*).\n",
+    "        # 0 pour les algorithmes qui n'iterent pas (Greedy, A*, BFS, ...).\n",
+    "        self.iterations = iterations\n",
     "\n",
     "    @property\n",
     "    def found(self):\n",
@@ -332,6 +336,8 @@
     "        print(f\"  Noeuds explores  : {self.nodes_expanded}\")\n",
     "        print(f\"  Noeuds generes   : {self.nodes_generated}\")\n",
     "        print(f\"  Frontiere max    : {self.max_frontier_size}\")\n",
+    "        if self.iterations > 0:\n",
+    "            print(f\"  Iterations       : {self.iterations}\")\n",
     "        print(f\"  Temps            : {self.elapsed_ms:.2f} ms\")\n",
     "\n",
     "\n",
@@ -343,10 +349,10 @@
    "id": "problem-example-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.008892,
-     "end_time": "2026-10-05T11:06:22.050469+00:00",
+     "duration": 0.006055,
+     "end_time": "2026-10-20T06:27:56.012336+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.041577+00:00",
+     "start_time": "2026-10-20T06:27:56.006281+00:00",
      "status": "completed"
     },
     "tags": []
@@ -372,16 +378,16 @@
    "id": "graph-problem",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:22.064155Z",
-     "iopub.status.busy": "2026-10-05T11:06:22.063468Z",
-     "iopub.status.idle": "2026-10-05T11:06:22.070596Z",
-     "shell.execute_reply": "2026-10-05T11:06:22.070124Z"
+     "iopub.execute_input": "2026-10-20T06:27:56.027009Z",
+     "iopub.status.busy": "2026-10-20T06:27:56.026781Z",
+     "iopub.status.idle": "2026-10-20T06:27:56.033655Z",
+     "shell.execute_reply": "2026-10-20T06:27:56.032896Z"
     },
     "papermill": {
-     "duration": 0.015218,
-     "end_time": "2026-10-05T11:06:22.071751+00:00",
+     "duration": 0.015852,
+     "end_time": "2026-10-20T06:27:56.034631+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.056533+00:00",
+     "start_time": "2026-10-20T06:27:56.018779+00:00",
      "status": "completed"
     },
     "tags": []
@@ -461,10 +467,10 @@
    "id": "heuristic-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.006065,
-     "end_time": "2026-10-05T11:06:22.084242+00:00",
+     "duration": 0.006478,
+     "end_time": "2026-10-20T06:27:56.049273+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.078177+00:00",
+     "start_time": "2026-10-20T06:27:56.042795+00:00",
      "status": "completed"
     },
     "tags": []
@@ -499,16 +505,16 @@
    "id": "heuristic-function",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:22.098439Z",
-     "iopub.status.busy": "2026-10-05T11:06:22.098032Z",
-     "iopub.status.idle": "2026-10-05T11:06:22.102685Z",
-     "shell.execute_reply": "2026-10-05T11:06:22.102166Z"
+     "iopub.execute_input": "2026-10-20T06:27:56.063973Z",
+     "iopub.status.busy": "2026-10-20T06:27:56.063740Z",
+     "iopub.status.idle": "2026-10-20T06:27:56.069495Z",
+     "shell.execute_reply": "2026-10-20T06:27:56.068724Z"
     },
     "papermill": {
-     "duration": 0.013145,
-     "end_time": "2026-10-05T11:06:22.103508+00:00",
+     "duration": 0.014215,
+     "end_time": "2026-10-20T06:27:56.070187+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.090363+00:00",
+     "start_time": "2026-10-20T06:27:56.055972+00:00",
      "status": "completed"
     },
     "tags": []
@@ -568,10 +574,10 @@
    "id": "greedy-section",
    "metadata": {
     "papermill": {
-     "duration": 0.006317,
-     "end_time": "2026-10-05T11:06:22.117590+00:00",
+     "duration": 0.006498,
+     "end_time": "2026-10-20T06:27:56.083540+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.111273+00:00",
+     "start_time": "2026-10-20T06:27:56.077042+00:00",
      "status": "completed"
     },
     "tags": []
@@ -591,11 +597,14 @@
     "\n",
     "| Propriété | Valeur | Condition |\n",
     "|-----------|--------|----------|\n",
-    "| Complétude | Non | Peut boucler dans les graphes finis |\n",
+    "| Complétude (tree-search) | Non | Peut boucler indéfiniment sur un graphe à cycles |\n",
+    "| Complétude (graph-search, closed set) | Oui (graphe fini) | Variante implémentée dans ce notebook (cellule 10) : `explored` empêche les revisites |\n",
     "| Optimalité | Non | Minimise $h(n)$, pas le coût réel |\n",
     "| Complexité temporelle | $O(b^m)$ | Pire cas |\n",
     "| Complexité spatiale | $O(b^m)$ | Pire cas |\n",
     "\n",
+    "> **Note** : la complétude dépend de la **variante d'implémentation**. La distinction tree vs graph-search s'applique à tous les algorithmes informés — A* utilise ici la même variante graph-search (sans ré-ouverture) et applique la même nuance. Sur des espaces d'états non bornés (graphe infini), Greedy n'est pas complet, ni en tree ni en graph-search.\n",
+    "\n",
     "En pratique, Greedy est souvent très rapide mais ne garantit pas la qualité de la solution.\n"
    ]
   },
@@ -605,16 +614,16 @@
    "id": "greedy-impl",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:22.132933Z",
-     "iopub.status.busy": "2026-10-05T11:06:22.132115Z",
-     "iopub.status.idle": "2026-10-05T11:06:22.140040Z",
-     "shell.execute_reply": "2026-10-05T11:06:22.139352Z"
+     "iopub.execute_input": "2026-10-20T06:27:56.097489Z",
+     "iopub.status.busy": "2026-10-20T06:27:56.097235Z",
+     "iopub.status.idle": "2026-10-20T06:27:56.104093Z",
+     "shell.execute_reply": "2026-10-20T06:27:56.103053Z"
     },
     "papermill": {
-     "duration": 0.017161,
-     "end_time": "2026-10-05T11:06:22.140941+00:00",
+     "duration": 0.015077,
+     "end_time": "2026-10-20T06:27:56.105054+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.123780+00:00",
+     "start_time": "2026-10-20T06:27:56.089977+00:00",
      "status": "completed"
     },
     "tags": []
@@ -704,10 +713,10 @@
    "id": "340234eb",
    "metadata": {
     "papermill": {
-     "duration": 0.008081,
-     "end_time": "2026-10-05T11:06:22.155554+00:00",
+     "duration": 0.006243,
+     "end_time": "2026-10-20T06:27:56.117904+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.147473+00:00",
+     "start_time": "2026-10-20T06:27:56.111661+00:00",
      "status": "completed"
     },
     "tags": []
@@ -730,16 +739,16 @@
    "id": "greedy-test",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:22.180244Z",
-     "iopub.status.busy": "2026-10-05T11:06:22.179489Z",
-     "iopub.status.idle": "2026-10-05T11:06:22.187585Z",
-     "shell.execute_reply": "2026-10-05T11:06:22.186319Z"
+     "iopub.execute_input": "2026-10-20T06:27:56.134362Z",
+     "iopub.status.busy": "2026-10-20T06:27:56.134111Z",
+     "iopub.status.idle": "2026-10-20T06:27:56.138157Z",
+     "shell.execute_reply": "2026-10-20T06:27:56.137137Z"
     },
     "papermill": {
-     "duration": 0.021788,
-     "end_time": "2026-10-05T11:06:22.189272+00:00",
+     "duration": 0.012565,
+     "end_time": "2026-10-20T06:27:56.138810+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.167484+00:00",
+     "start_time": "2026-10-20T06:27:56.126245+00:00",
      "status": "completed"
     },
     "tags": []
@@ -761,7 +770,7 @@
       "  Noeuds explores  : 2\n",
       "  Noeuds generes   : 8\n",
       "  Frontiere max    : 5\n",
-      "  Temps            : 0.14 ms\n"
+      "  Temps            : 0.08 ms\n"
      ]
     }
    ],
@@ -779,10 +788,10 @@
    "id": "dcjquopv3vv",
    "metadata": {
     "papermill": {
-     "duration": 0.011973,
-     "end_time": "2026-10-05T11:06:22.211967+00:00",
+     "duration": 0.006274,
+     "end_time": "2026-10-20T06:27:56.152153+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.199994+00:00",
+     "start_time": "2026-10-20T06:27:56.145879+00:00",
      "status": "completed"
     },
     "tags": []
@@ -809,10 +818,10 @@
    "id": "greedy-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.01038,
-     "end_time": "2026-10-05T11:06:22.234803+00:00",
+     "duration": 0.006458,
+     "end_time": "2026-10-20T06:27:56.166940+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.224423+00:00",
+     "start_time": "2026-10-20T06:27:56.160482+00:00",
      "status": "completed"
     },
     "tags": []
@@ -839,10 +848,10 @@
    "id": "astar-section",
    "metadata": {
     "papermill": {
-     "duration": 0.015471,
-     "end_time": "2026-10-05T11:06:22.264310+00:00",
+     "duration": 0.006764,
+     "end_time": "2026-10-20T06:27:56.180330+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.248839+00:00",
+     "start_time": "2026-10-20T06:27:56.173566+00:00",
      "status": "completed"
     },
     "tags": []
@@ -890,16 +899,16 @@
    "id": "astar-impl",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:22.300958Z",
-     "iopub.status.busy": "2026-10-05T11:06:22.300240Z",
-     "iopub.status.idle": "2026-10-05T11:06:22.314324Z",
-     "shell.execute_reply": "2026-10-05T11:06:22.313047Z"
+     "iopub.execute_input": "2026-10-20T06:27:56.194874Z",
+     "iopub.status.busy": "2026-10-20T06:27:56.194676Z",
+     "iopub.status.idle": "2026-10-20T06:27:56.201158Z",
+     "shell.execute_reply": "2026-10-20T06:27:56.200297Z"
     },
     "papermill": {
-     "duration": 0.03486,
-     "end_time": "2026-10-05T11:06:22.316988+00:00",
+     "duration": 0.014949,
+     "end_time": "2026-10-20T06:27:56.201746+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.282128+00:00",
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      "status": "completed"
     },
     "tags": []
@@ -992,10 +1001,10 @@
    "id": "8a172994",
    "metadata": {
     "papermill": {
-     "duration": 0.010986,
-     "end_time": "2026-10-05T11:06:22.341512+00:00",
+     "duration": 0.006315,
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      "exception": false,
-     "start_time": "2026-10-05T11:06:22.330526+00:00",
+     "start_time": "2026-10-20T06:27:56.208618+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1019,16 +1028,16 @@
    "id": "astar-test",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:22.372727Z",
-     "iopub.status.busy": "2026-10-05T11:06:22.371654Z",
-     "iopub.status.idle": "2026-10-05T11:06:22.378351Z",
-     "shell.execute_reply": "2026-10-05T11:06:22.377010Z"
+     "iopub.execute_input": "2026-10-20T06:27:56.231481Z",
+     "iopub.status.busy": "2026-10-20T06:27:56.231251Z",
+     "iopub.status.idle": "2026-10-20T06:27:56.235050Z",
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     },
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-     "end_time": "2026-10-05T11:06:22.381163+00:00",
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      "exception": false,
-     "start_time": "2026-10-05T11:06:22.354105+00:00",
+     "start_time": "2026-10-20T06:27:56.223835+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1051,7 +1060,7 @@
       "  Noeuds explores  : 3\n",
       "  Noeuds generes   : 11\n",
       "  Frontiere max    : 6\n",
-      "  Temps            : 0.20 ms\n"
+      "  Temps            : 0.08 ms\n"
      ]
     }
    ],
@@ -1069,10 +1078,10 @@
    "id": "1mddry1upc6",
    "metadata": {
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-     "end_time": "2026-10-05T11:06:22.406248+00:00",
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      "exception": false,
-     "start_time": "2026-10-05T11:06:22.393698+00:00",
+     "start_time": "2026-10-20T06:27:56.242673+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1096,10 +1105,10 @@
    "id": "astar-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.006214,
-     "end_time": "2026-10-05T11:06:22.421497+00:00",
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      "exception": false,
-     "start_time": "2026-10-05T11:06:22.415283+00:00",
+     "start_time": "2026-10-20T06:27:56.255692+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1125,10 +1134,10 @@
    "id": "comparison-section",
    "metadata": {
     "papermill": {
-     "duration": 0.005972,
-     "end_time": "2026-10-05T11:06:22.433402+00:00",
+     "duration": 0.008084,
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      "exception": false,
-     "start_time": "2026-10-05T11:06:22.427430+00:00",
+     "start_time": "2026-10-20T06:27:56.268575+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1153,16 +1162,16 @@
    "id": "comparison-greedy-astar",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:22.446562Z",
-     "iopub.status.busy": "2026-10-05T11:06:22.446336Z",
-     "iopub.status.idle": "2026-10-05T11:06:22.471047Z",
-     "shell.execute_reply": "2026-10-05T11:06:22.470384Z"
+     "iopub.execute_input": "2026-10-20T06:27:56.293153Z",
+     "iopub.status.busy": "2026-10-20T06:27:56.292917Z",
+     "iopub.status.idle": "2026-10-20T06:27:56.325038Z",
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     },
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-     "end_time": "2026-10-05T11:06:22.471904+00:00",
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      "exception": false,
-     "start_time": "2026-10-05T11:06:22.439468+00:00",
+     "start_time": "2026-10-20T06:27:56.283222+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1173,9 +1182,15 @@
      "output_type": "stream",
      "text": [
       "Comparaison : Greedy vs A*\n",
-      "======================================================================\n",
+      "======================================================================\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Algorithme                          Chemin  Cout (km)  Longueur  Noeuds explores Temps (ms)\n",
-      "    Greedy Bordeaux -> Paris -> Strasbourg       1075         2                2       0.03\n",
+      "    Greedy Bordeaux -> Paris -> Strasbourg       1075         2                2       0.05\n",
       "        A* Bordeaux -> Paris -> Strasbourg       1075         2                3       0.02\n",
       "\n",
       "======================================================================\n",
@@ -1231,10 +1246,10 @@
    "id": "6ab7895c",
    "metadata": {
     "papermill": {
-     "duration": 0.006711,
-     "end_time": "2026-10-05T11:06:22.485463+00:00",
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      "exception": false,
-     "start_time": "2026-10-05T11:06:22.478752+00:00",
+     "start_time": "2026-10-20T06:27:56.332992+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1260,16 +1275,16 @@
    "id": "ca315179",
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2026-10-05T11:06:22.498082Z",
-     "iopub.status.idle": "2026-10-05T11:06:22.505901Z",
-     "shell.execute_reply": "2026-10-05T11:06:22.505312Z"
+     "iopub.execute_input": "2026-10-20T06:27:56.355026Z",
+     "iopub.status.busy": "2026-10-20T06:27:56.354811Z",
+     "iopub.status.idle": "2026-10-20T06:27:56.363115Z",
+     "shell.execute_reply": "2026-10-20T06:27:56.362387Z"
     },
     "papermill": {
-     "duration": 0.015655,
-     "end_time": "2026-10-05T11:06:22.506986+00:00",
+     "duration": 0.017113,
+     "end_time": "2026-10-20T06:27:56.363737+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.491331+00:00",
+     "start_time": "2026-10-20T06:27:56.346624+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1282,7 +1297,7 @@
       "Comparaison : Greedy vs A* sur Nice -> Bordeaux\n",
       "======================================================================\n",
       "Algorithme                                        Chemin  Cout (km)  Longueur  Noeuds explores Temps (ms)\n",
-      "    Greedy Nice -> Grenoble -> Lyon -> Paris -> Bordeaux       1490         4                4       0.04\n",
+      "    Greedy Nice -> Grenoble -> Lyon -> Paris -> Bordeaux       1490         4                4       0.06\n",
       "        A*     Nice -> Marseille -> Toulouse -> Bordeaux        850         3                5       0.03\n",
       "\n",
       "======================================================================\n",
@@ -1341,10 +1356,10 @@
    "id": "comparison-interpretation",
    "metadata": {
     "papermill": {
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-     "end_time": "2026-10-05T11:06:22.526155+00:00",
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      "exception": false,
-     "start_time": "2026-10-05T11:06:22.514182+00:00",
+     "start_time": "2026-10-20T06:27:56.371665+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1368,10 +1383,10 @@
    "id": "puzzle-section",
    "metadata": {
     "papermill": {
-     "duration": 0.012904,
-     "end_time": "2026-10-05T11:06:22.548175+00:00",
+     "duration": 0.009087,
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      "exception": false,
-     "start_time": "2026-10-05T11:06:22.535271+00:00",
+     "start_time": "2026-10-20T06:27:56.387048+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1413,16 +1428,16 @@
    "id": "puzzle-class",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:22.575636Z",
-     "iopub.status.busy": "2026-10-05T11:06:22.574852Z",
-     "iopub.status.idle": "2026-10-05T11:06:22.592268Z",
-     "shell.execute_reply": "2026-10-05T11:06:22.591024Z"
+     "iopub.execute_input": "2026-10-20T06:27:56.412820Z",
+     "iopub.status.busy": "2026-10-20T06:27:56.412604Z",
+     "iopub.status.idle": "2026-10-20T06:27:56.421137Z",
+     "shell.execute_reply": "2026-10-20T06:27:56.420241Z"
     },
     "papermill": {
-     "duration": 0.033571,
-     "end_time": "2026-10-05T11:06:22.594120+00:00",
+     "duration": 0.017655,
+     "end_time": "2026-10-20T06:27:56.421783+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.560549+00:00",
+     "start_time": "2026-10-20T06:27:56.404128+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1560,10 +1575,10 @@
    "id": "b3189c7b",
    "metadata": {
     "papermill": {
-     "duration": 0.012234,
-     "end_time": "2026-10-05T11:06:22.620426+00:00",
+     "duration": 0.006608,
+     "end_time": "2026-10-20T06:27:56.435309+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.608192+00:00",
+     "start_time": "2026-10-20T06:27:56.428701+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1594,16 +1609,16 @@
    "id": "puzzle-benchmark",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:22.647939Z",
-     "iopub.status.busy": "2026-10-05T11:06:22.647414Z",
-     "iopub.status.idle": "2026-10-05T11:06:24.864606Z",
-     "shell.execute_reply": "2026-10-05T11:06:24.863962Z"
+     "iopub.execute_input": "2026-10-20T06:27:56.456944Z",
+     "iopub.status.busy": "2026-10-20T06:27:56.456727Z",
+     "iopub.status.idle": "2026-10-20T06:27:58.191140Z",
+     "shell.execute_reply": "2026-10-20T06:27:58.190284Z"
     },
     "papermill": {
-     "duration": 2.234828,
-     "end_time": "2026-10-05T11:06:24.865763+00:00",
+     "duration": 1.748601,
+     "end_time": "2026-10-20T06:27:58.195957+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:22.630935+00:00",
+     "start_time": "2026-10-20T06:27:56.447356+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1623,8 +1638,8 @@
       "\n",
       "Heuristique              Noeuds     Cout   Temps (ms)\n",
       "----------------------------------------------------------------------\n",
-      "Tuiles mal placees            1        1         0.07\n",
-      "Distance Manhattan            1        1         0.04\n",
+      "Tuiles mal placees            1        1         0.06\n",
+      "Distance Manhattan            1        1         0.02\n",
       "\n",
       "Reduction des noeuds explores : 0.0%\n",
       "\n",
@@ -1635,8 +1650,8 @@
       "\n",
       "Heuristique              Noeuds     Cout   Temps (ms)\n",
       "----------------------------------------------------------------------\n",
-      "Tuiles mal placees          770       16         9.61\n",
-      "Distance Manhattan          220       16         3.07\n",
+      "Tuiles mal placees          770       16         5.35\n",
+      "Distance Manhattan          220       16         1.99\n",
       "\n",
       "Reduction des noeuds explores : 71.4%\n",
       "\n",
@@ -1653,8 +1668,8 @@
       "\n",
       "Heuristique              Noeuds     Cout   Temps (ms)\n",
       "----------------------------------------------------------------------\n",
-      "Tuiles mal placees       143839       31      1852.67\n",
-      "Distance Manhattan        20290       31       317.23\n",
+      "Tuiles mal placees       143839       31      1514.62\n",
+      "Distance Manhattan        20290       31       183.01\n",
       "\n",
       "Reduction des noeuds explores : 85.9%\n"
      ]
@@ -1662,8 +1677,8 @@
     {
      "data": {
       "text/plain": [
-       "(<__main__.SearchResult at 0x16da668fa70>,\n",
-       " <__main__.SearchResult at 0x16da668f980>)"
+       "(<__main__.SearchResult at 0x1a3cba206e0>,\n",
+       " <__main__.SearchResult at 0x1a3cba207d0>)"
       ]
      },
      "execution_count": 12,
@@ -1715,10 +1730,10 @@
    "id": "k292t2mslp",
    "metadata": {
     "papermill": {
-     "duration": 0.01156,
-     "end_time": "2026-10-05T11:06:24.884444+00:00",
+     "duration": 0.007297,
+     "end_time": "2026-10-20T06:27:58.210983+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:24.872884+00:00",
+     "start_time": "2026-10-20T06:27:58.203686+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1743,10 +1758,10 @@
    "id": "puzzle-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.009761,
-     "end_time": "2026-10-05T11:06:24.905401+00:00",
+     "duration": 0.00715,
+     "end_time": "2026-10-20T06:27:58.225960+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:24.895640+00:00",
+     "start_time": "2026-10-20T06:27:58.218810+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1774,10 +1789,10 @@
    "id": "d7b928ea",
    "metadata": {
     "papermill": {
-     "duration": 0.00743,
-     "end_time": "2026-10-05T11:06:24.920376+00:00",
+     "duration": 0.007357,
+     "end_time": "2026-10-20T06:27:58.240902+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:24.912946+00:00",
+     "start_time": "2026-10-20T06:27:58.233545+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1809,16 +1824,16 @@
    "id": "b55555ba",
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2026-10-05T11:06:24.936213Z",
-     "iopub.status.idle": "2026-10-05T11:06:24.940397Z",
-     "shell.execute_reply": "2026-10-05T11:06:24.939740Z"
+     "iopub.execute_input": "2026-10-20T06:27:58.257771Z",
+     "iopub.status.busy": "2026-10-20T06:27:58.257534Z",
+     "iopub.status.idle": "2026-10-20T06:27:58.261558Z",
+     "shell.execute_reply": "2026-10-20T06:27:58.260581Z"
     },
     "papermill": {
-     "duration": 0.013056,
-     "end_time": "2026-10-05T11:06:24.941372+00:00",
+     "duration": 0.015199,
+     "end_time": "2026-10-20T06:27:58.263897+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:24.928316+00:00",
+     "start_time": "2026-10-20T06:27:58.248698+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1852,10 +1867,10 @@
    "id": "visualization-section",
    "metadata": {
     "papermill": {
-     "duration": 0.006656,
-     "end_time": "2026-10-05T11:06:24.954563+00:00",
+     "duration": 0.006826,
+     "end_time": "2026-10-20T06:27:58.277773+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:24.947907+00:00",
+     "start_time": "2026-10-20T06:27:58.270947+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1881,16 +1896,16 @@
    "id": "puzzle-visualization",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:24.968967Z",
-     "iopub.status.busy": "2026-10-05T11:06:24.968450Z",
-     "iopub.status.idle": "2026-10-05T11:06:25.472065Z",
-     "shell.execute_reply": "2026-10-05T11:06:25.469913Z"
+     "iopub.execute_input": "2026-10-20T06:27:58.293726Z",
+     "iopub.status.busy": "2026-10-20T06:27:58.293458Z",
+     "iopub.status.idle": "2026-10-20T06:27:58.688078Z",
+     "shell.execute_reply": "2026-10-20T06:27:58.687449Z"
     },
     "papermill": {
-     "duration": 0.512767,
-     "end_time": "2026-10-05T11:06:25.473687+00:00",
+     "duration": 0.403867,
+     "end_time": "2026-10-20T06:27:58.688624+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:24.960920+00:00",
+     "start_time": "2026-10-20T06:27:58.284757+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1898,7 +1913,7 @@
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 900x600 with 6 Axes>"
       ]
@@ -2012,10 +2027,10 @@
    "id": "visualization-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.012291,
-     "end_time": "2026-10-05T11:06:25.497508+00:00",
+     "duration": 0.007394,
+     "end_time": "2026-10-20T06:27:58.703797+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.485217+00:00",
+     "start_time": "2026-10-20T06:27:58.696403+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2038,10 +2053,10 @@
    "id": "ida-section",
    "metadata": {
     "papermill": {
-     "duration": 0.011853,
-     "end_time": "2026-10-05T11:06:25.524021+00:00",
+     "duration": 0.007568,
+     "end_time": "2026-10-20T06:27:58.719087+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.512168+00:00",
+     "start_time": "2026-10-20T06:27:58.711519+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2082,16 +2097,16 @@
    "id": "idastar-impl",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:25.553445Z",
-     "iopub.status.busy": "2026-10-05T11:06:25.553112Z",
-     "iopub.status.idle": "2026-10-05T11:06:25.566015Z",
-     "shell.execute_reply": "2026-10-05T11:06:25.564072Z"
+     "iopub.execute_input": "2026-10-20T06:27:58.736636Z",
+     "iopub.status.busy": "2026-10-20T06:27:58.736443Z",
+     "iopub.status.idle": "2026-10-20T06:27:58.742760Z",
+     "shell.execute_reply": "2026-10-20T06:27:58.742036Z"
     },
     "papermill": {
-     "duration": 0.031566,
-     "end_time": "2026-10-05T11:06:25.567848+00:00",
+     "duration": 0.015501,
+     "end_time": "2026-10-20T06:27:58.743494+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.536282+00:00",
+     "start_time": "2026-10-20T06:27:58.727993+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2121,13 +2136,18 @@
     "    explored_order = []\n",
     "    iterations = 0\n",
     "\n",
-    "    def search(node, bound, min_excess):\n",
+    "    def search(node, bound, depth, max_depth_holder):\n",
     "        \"\"\"\n",
     "        DFS recursif avec limite sur f(n).\n",
     "        Retourne (solution_node, new_bound).\n",
+    "        Met a jour max_depth_holder[0] avec la profondeur recursive max observee.\n",
+    "        La profondeur recursive est la mesure reelle de la memoire d'IDA* (en O(bd)).\n",
     "        \"\"\"\n",
     "        nonlocal total_expanded, total_generated\n",
     "\n",
+    "        if depth > max_depth_holder[0]:\n",
+    "            max_depth_holder[0] = depth\n",
+    "\n",
     "        f = node.f\n",
     "        if f > bound:\n",
     "            return None, f  # Coupe : retourner le f qui a provoque la coupure\n",
@@ -2140,7 +2160,7 @@
     "\n",
     "        for child in node.expand(problem, heuristic):\n",
     "            total_generated += 1\n",
-    "            result, child_bound = search(child, bound, min_next_bound)\n",
+    "            result, child_bound = search(child, bound, depth + 1, max_depth_holder)\n",
     "\n",
     "            if result is not None:\n",
     "                return result, bound\n",
@@ -2149,6 +2169,10 @@
     "\n",
     "        return None, min_next_bound\n",
     "\n",
+    "    # Memoire reelle d'IDA* = profondeur de recursion. On utilise un holder\n",
+    "    # mutable (la profondeur est passee/revenue par la pile d'appels).\n",
+    "    max_depth_holder = [0]\n",
+    "\n",
     "    # Boucle principale avec bornes croissantes\n",
     "    while iterations < max_iterations:\n",
     "        iterations += 1\n",
@@ -2156,15 +2180,22 @@
     "        if verbose:\n",
     "            print(f\"  Iteration {iterations}: bound = {bound:.0f}\")\n",
     "\n",
-    "        result, new_bound = search(root, bound, float('inf'))\n",
+    "        result, new_bound = search(root, bound, 0, max_depth_holder)\n",
     "\n",
     "        if result is not None:\n",
     "            elapsed = (time.perf_counter() - start_time) * 1000\n",
     "            # Reconstruire l'ordre d'exploration (approximatif)\n",
+    "            # NB : 5eme slot = max_frontier_size. Pour IDA* la \"frontiere\"\n",
+    "            # n'existe pas, on y passe la profondeur recursive max (memoire\n",
+    "            # de pile reelle, en O(bd)). `iterations` (nb de bornes f\n",
+    "            # explorees) est expose separement.\n",
     "            return SearchResult('IDA*', result, total_expanded,\n",
-    "                                total_generated, iterations,\n",
-    "                                elapsed, explored_order,\n",
-    "                                heuristic.__name__)\n",
+    "                                total_generated,\n",
+    "                                max_frontier_size=max_depth_holder[0],\n",
+    "                                elapsed_ms=elapsed,\n",
+    "                                explored_order=explored_order,\n",
+    "                                heuristic_name=heuristic.__name__,\n",
+    "                                iterations=iterations)\n",
     "\n",
     "        if new_bound == float('inf'):\n",
     "            break  # Espace entier explore\n",
@@ -2173,8 +2204,11 @@
     "\n",
     "    elapsed = (time.perf_counter() - start_time) * 1000\n",
     "    return SearchResult('IDA*', None, total_expanded, total_generated,\n",
-    "                        0, elapsed, explored_order,\n",
-    "                        heuristic.__name__)\n",
+    "                        max_frontier_size=max_depth_holder[0],\n",
+    "                        elapsed_ms=elapsed,\n",
+    "                        explored_order=explored_order,\n",
+    "                        heuristic_name=heuristic.__name__,\n",
+    "                        iterations=iterations)\n",
     "\n",
     "\n",
     "print(\"Algorithme IDA* implemente.\")"
@@ -2185,10 +2219,10 @@
    "id": "40f8a8e5",
    "metadata": {
     "papermill": {
-     "duration": 0.012726,
-     "end_time": "2026-10-05T11:06:25.594129+00:00",
+     "duration": 0.006877,
+     "end_time": "2026-10-20T06:27:58.757921+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.581403+00:00",
+     "start_time": "2026-10-20T06:27:58.751044+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2200,7 +2234,7 @@
     "quatre itérations depth-first, chacune bornée par la plus petite valeur de\n",
     "$f$ qui avait dépassé la borne précédente — **784, puis 993, puis 1018, puis\n",
     "1075**. Chaque itération ré-explore l'arbre depuis la racine. L'échange\n",
-    "structurel se lit dans les compteurs : frontière maximale de **4** seulement\n",
+    "structurel se lit dans les compteurs : frontière maximale de **2** seulement\n",
     "(contre 6 pour A\\*), au prix de **8 nœuds explorés** (contre 3). IDA\\* troque\n",
     "la mémoire de la frontière contre du calcul répété — le bon marché quand la\n",
     "frontière ne tient pas en mémoire, le mauvais quand les bornes se resserrent\n",
@@ -2213,16 +2247,16 @@
    "id": "idastar-test",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:25.625559Z",
-     "iopub.status.busy": "2026-10-05T11:06:25.624976Z",
-     "iopub.status.idle": "2026-10-05T11:06:25.632350Z",
-     "shell.execute_reply": "2026-10-05T11:06:25.631101Z"
+     "iopub.execute_input": "2026-10-20T06:27:58.772543Z",
+     "iopub.status.busy": "2026-10-20T06:27:58.772326Z",
+     "iopub.status.idle": "2026-10-20T06:27:58.776702Z",
+     "shell.execute_reply": "2026-10-20T06:27:58.775725Z"
     },
     "papermill": {
-     "duration": 0.026061,
-     "end_time": "2026-10-05T11:06:25.634384+00:00",
+     "duration": 0.012781,
+     "end_time": "2026-10-20T06:27:58.777357+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.608323+00:00",
+     "start_time": "2026-10-20T06:27:58.764576+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2245,8 +2279,9 @@
       "  Longueur   : 2 etapes\n",
       "  Noeuds explores  : 8\n",
       "  Noeuds generes   : 24\n",
-      "  Frontiere max    : 4\n",
-      "  Temps            : 0.19 ms\n"
+      "  Frontiere max    : 2\n",
+      "  Iterations       : 4\n",
+      "  Temps            : 0.12 ms\n"
      ]
     }
    ],
@@ -2264,10 +2299,10 @@
    "id": "0gzwacp0hfbq",
    "metadata": {
     "papermill": {
-     "duration": 0.012977,
-     "end_time": "2026-10-05T11:06:25.661132+00:00",
+     "duration": 0.0071,
+     "end_time": "2026-10-20T06:27:58.791391+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.648155+00:00",
+     "start_time": "2026-10-20T06:27:58.784291+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2291,10 +2326,10 @@
    "id": "idastar-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.015727,
-     "end_time": "2026-10-05T11:06:25.689540+00:00",
+     "duration": 0.006822,
+     "end_time": "2026-10-20T06:27:58.805234+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.673813+00:00",
+     "start_time": "2026-10-20T06:27:58.798412+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2322,10 +2357,10 @@
    "id": "752d920b",
    "metadata": {
     "papermill": {
-     "duration": 0.007028,
-     "end_time": "2026-10-05T11:06:25.704611+00:00",
+     "duration": 0.006708,
+     "end_time": "2026-10-20T06:27:58.820107+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.697583+00:00",
+     "start_time": "2026-10-20T06:27:58.813399+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2351,16 +2386,16 @@
    "id": "b119ca90",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:25.721110Z",
-     "iopub.status.busy": "2026-10-05T11:06:25.720557Z",
-     "iopub.status.idle": "2026-10-05T11:06:25.725587Z",
-     "shell.execute_reply": "2026-10-05T11:06:25.724500Z"
+     "iopub.execute_input": "2026-10-20T06:27:58.836143Z",
+     "iopub.status.busy": "2026-10-20T06:27:58.835849Z",
+     "iopub.status.idle": "2026-10-20T06:27:58.839794Z",
+     "shell.execute_reply": "2026-10-20T06:27:58.838834Z"
     },
     "papermill": {
-     "duration": 0.014687,
-     "end_time": "2026-10-05T11:06:25.726899+00:00",
+     "duration": 0.013155,
+     "end_time": "2026-10-20T06:27:58.840784+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.712212+00:00",
+     "start_time": "2026-10-20T06:27:58.827629+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2391,10 +2426,10 @@
    "id": "all-comparison-section",
    "metadata": {
     "papermill": {
-     "duration": 0.007764,
-     "end_time": "2026-10-05T11:06:25.743462+00:00",
+     "duration": 0.007826,
+     "end_time": "2026-10-20T06:27:58.856343+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.735698+00:00",
+     "start_time": "2026-10-20T06:27:58.848517+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2428,16 +2463,16 @@
    "id": "all-comparison",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:25.759781Z",
-     "iopub.status.busy": "2026-10-05T11:06:25.759181Z",
-     "iopub.status.idle": "2026-10-05T11:06:25.768061Z",
-     "shell.execute_reply": "2026-10-05T11:06:25.766581Z"
+     "iopub.execute_input": "2026-10-20T06:27:58.874152Z",
+     "iopub.status.busy": "2026-10-20T06:27:58.873939Z",
+     "iopub.status.idle": "2026-10-20T06:27:58.881759Z",
+     "shell.execute_reply": "2026-10-20T06:27:58.880910Z"
     },
     "papermill": {
-     "duration": 0.018694,
-     "end_time": "2026-10-05T11:06:25.769610+00:00",
+     "duration": 0.018435,
+     "end_time": "2026-10-20T06:27:58.882612+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.750916+00:00",
+     "start_time": "2026-10-20T06:27:58.864177+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2535,10 +2570,10 @@
    "id": "all-comparison-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.007153,
-     "end_time": "2026-10-05T11:06:25.783861+00:00",
+     "duration": 0.007259,
+     "end_time": "2026-10-20T06:27:58.898539+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.776708+00:00",
+     "start_time": "2026-10-20T06:27:58.891280+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2567,10 +2602,10 @@
    "id": "heuristic-design-section",
    "metadata": {
     "papermill": {
-     "duration": 0.008441,
-     "end_time": "2026-10-05T11:06:25.799387+00:00",
+     "duration": 0.007639,
+     "end_time": "2026-10-20T06:27:58.913955+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.790946+00:00",
+     "start_time": "2026-10-20T06:27:58.906316+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2597,10 +2632,10 @@
    "id": "admissibility-section",
    "metadata": {
     "papermill": {
-     "duration": 0.00969,
-     "end_time": "2026-10-05T11:06:25.815857+00:00",
+     "duration": 0.008058,
+     "end_time": "2026-10-20T06:27:58.929900+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.806167+00:00",
+     "start_time": "2026-10-20T06:27:58.921842+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2631,10 +2666,10 @@
    "id": "consistency-section",
    "metadata": {
     "papermill": {
-     "duration": 0.007058,
-     "end_time": "2026-10-05T11:06:25.830191+00:00",
+     "duration": 0.007861,
+     "end_time": "2026-10-20T06:27:58.945779+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.823133+00:00",
+     "start_time": "2026-10-20T06:27:58.937918+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2666,10 +2701,10 @@
    "id": "counterexample-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.006915,
-     "end_time": "2026-10-05T11:06:25.844003+00:00",
+     "duration": 0.00767,
+     "end_time": "2026-10-20T06:27:58.961213+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.837088+00:00",
+     "start_time": "2026-10-20T06:27:58.953543+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2688,16 +2723,16 @@
    "id": "counterexample-demo",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:25.861912Z",
-     "iopub.status.busy": "2026-10-05T11:06:25.861614Z",
-     "iopub.status.idle": "2026-10-05T11:06:25.868435Z",
-     "shell.execute_reply": "2026-10-05T11:06:25.867997Z"
+     "iopub.execute_input": "2026-10-20T06:27:58.980745Z",
+     "iopub.status.busy": "2026-10-20T06:27:58.980495Z",
+     "iopub.status.idle": "2026-10-20T06:27:58.987936Z",
+     "shell.execute_reply": "2026-10-20T06:27:58.987291Z"
     },
     "papermill": {
-     "duration": 0.017617,
-     "end_time": "2026-10-05T11:06:25.869247+00:00",
+     "duration": 0.019151,
+     "end_time": "2026-10-20T06:27:58.988942+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.851630+00:00",
+     "start_time": "2026-10-20T06:27:58.969791+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2779,10 +2814,10 @@
    "id": "counterexample-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.010714,
-     "end_time": "2026-10-05T11:06:25.887342+00:00",
+     "duration": 0.006644,
+     "end_time": "2026-10-20T06:27:59.003766+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.876628+00:00",
+     "start_time": "2026-10-20T06:27:58.997122+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2802,10 +2837,10 @@
    "id": "dominance-section",
    "metadata": {
     "papermill": {
-     "duration": 0.012974,
-     "end_time": "2026-10-05T11:06:25.912442+00:00",
+     "duration": 0.006783,
+     "end_time": "2026-10-20T06:27:59.017247+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.899468+00:00",
+     "start_time": "2026-10-20T06:27:59.010464+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2836,10 +2871,10 @@
    "id": "summary-table-section",
    "metadata": {
     "papermill": {
-     "duration": 0.013561,
-     "end_time": "2026-10-05T11:06:25.938445+00:00",
+     "duration": 0.006731,
+     "end_time": "2026-10-20T06:27:59.031001+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.924884+00:00",
+     "start_time": "2026-10-20T06:27:59.024270+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2866,16 +2901,16 @@
    "id": "final-comparison",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:25.968942Z",
-     "iopub.status.busy": "2026-10-05T11:06:25.967942Z",
-     "iopub.status.idle": "2026-10-05T11:06:26.290929Z",
-     "shell.execute_reply": "2026-10-05T11:06:26.290414Z"
+     "iopub.execute_input": "2026-10-20T06:27:59.046329Z",
+     "iopub.status.busy": "2026-10-20T06:27:59.046104Z",
+     "iopub.status.idle": "2026-10-20T06:27:59.226193Z",
+     "shell.execute_reply": "2026-10-20T06:27:59.225393Z"
     },
     "papermill": {
-     "duration": 0.338004,
-     "end_time": "2026-10-05T11:06:26.291754+00:00",
+     "duration": 0.189084,
+     "end_time": "2026-10-20T06:27:59.226795+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:25.953750+00:00",
+     "start_time": "2026-10-20T06:27:59.037711+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2889,15 +2924,15 @@
       "Tableau recapitulatif : Algorithmes de recherche\n",
       "================================================================================\n",
       "Algorithme Complet            Optimal Cout  Noeuds explores Temps (ms)\n",
-      "       BFS     Oui                Non 1075                2       0.12\n",
-      "       UCS     Oui                Oui 1075               10       0.13\n",
-      "    Greedy     Non                Non 1075                2       0.07\n",
-      "        A*     Oui Oui (h admissible) 1075                3       0.05\n"
+      "       BFS     Oui                Non 1075                2       0.03\n",
+      "       UCS     Oui                Oui 1075               10       0.05\n",
+      "    Greedy     Oui                Non 1075                2       0.02\n",
+      "        A*     Oui Oui (h admissible) 1075                3       0.02\n"
      ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1400x500 with 2 Axes>"
       ]
@@ -2971,8 +3006,8 @@
     "        if problem.goal_test(current.state):\n",
     "            elapsed = (time.perf_counter() - start_time) * 1000\n",
     "            return SearchResult('UCS', current, nodes_expanded,\n",
-    "                                nodes_generated, max_frontier,\n",
-    "                                elapsed, explored_order)\n",
+    "                                nodes_generated,\n",
+    "                                max_frontier, elapsed, explored_order)\n",
     "\n",
     "        if current.state in explored:\n",
     "            continue\n",
@@ -3012,7 +3047,7 @@
     "for name, result in results.items():\n",
     "    summary_data.append({\n",
     "        'Algorithme': name,\n",
-    "        'Complet': 'Oui' if name in ['BFS', 'UCS', 'A*'] else 'Non',\n",
+    "        'Complet': 'Oui' if name in ['BFS', 'UCS', 'A*', 'Greedy'] else 'Non',\n",
     "        'Optimal': 'Oui' if name == 'UCS' else ('Oui (h admissible)' if name == 'A*' else 'Non'),\n",
     "        'Cout': f\"{result.cost:.0f}\",\n",
     "        'Noeuds explores': result.nodes_expanded,\n",
@@ -3060,10 +3095,10 @@
    "id": "summary-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.006835,
-     "end_time": "2026-10-05T11:06:26.305926+00:00",
+     "duration": 0.009587,
+     "end_time": "2026-10-20T06:27:59.245979+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.299091+00:00",
+     "start_time": "2026-10-20T06:27:59.236392+00:00",
      "status": "completed"
     },
     "tags": []
@@ -3079,12 +3114,13 @@
     "| DFS | Non | Non | En profondeur | Non |\n",
     "| UCS | Oui | Oui | Par coût croissant | Non |\n",
     "| IDDFS | Oui | Oui* | Profondeur itérée | Non |\n",
-    "| Greedy | Non | Non | Par $h(n)$ | Oui |\n",
+    "| Greedy (graph-search) | Oui*** | Non | Par $h(n)$ | Oui |\n",
     "| A* | Oui | Oui** | Par $g(n)+h(n)$ | Oui |\n",
     "| IDA* | Oui | Oui** | $f(n)$ itérée | Oui |\n",
     "\n",
     "\\* Optimal uniquement avec coûts uniformes.  \n",
     "\\*\\* A\\* : optimal avec heuristique **consistante** (variante Graph-Search sans ré-ouverture, contre-exemple §7) ; IDA\\* : optimal avec heuristique admissible (pas d'ensemble fermé).\n",
+    "\\*** Greedy (graph-search, closed set) : complet sur graphe fini avec successeurs finis (cf §3 Propriétés théoriques). En tree-search, Greedy n'est PAS complet (peut boucler sur un cycle).\n",
     "\n",
     "**Guide de choix pratique** :\n",
     "\n",
@@ -3102,10 +3138,10 @@
    "id": "exercises-section",
    "metadata": {
     "papermill": {
-     "duration": 0.006698,
-     "end_time": "2026-10-05T11:06:26.319483+00:00",
+     "duration": 0.011594,
+     "end_time": "2026-10-20T06:27:59.269451+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.312785+00:00",
+     "start_time": "2026-10-20T06:27:59.257857+00:00",
      "status": "completed"
     },
     "tags": []
@@ -3140,16 +3176,16 @@
    "id": "exercise1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:26.334223Z",
-     "iopub.status.busy": "2026-10-05T11:06:26.333866Z",
-     "iopub.status.idle": "2026-10-05T11:06:26.336800Z",
-     "shell.execute_reply": "2026-10-05T11:06:26.336357Z"
+     "iopub.execute_input": "2026-10-20T06:27:59.293047Z",
+     "iopub.status.busy": "2026-10-20T06:27:59.292748Z",
+     "iopub.status.idle": "2026-10-20T06:27:59.297803Z",
+     "shell.execute_reply": "2026-10-20T06:27:59.296901Z"
     },
     "papermill": {
-     "duration": 0.011545,
-     "end_time": "2026-10-05T11:06:26.337797+00:00",
+     "duration": 0.016911,
+     "end_time": "2026-10-20T06:27:59.298628+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.326252+00:00",
+     "start_time": "2026-10-20T06:27:59.281717+00:00",
      "status": "completed"
     },
     "tags": []
@@ -3172,10 +3208,10 @@
    "id": "exercise2",
    "metadata": {
     "papermill": {
-     "duration": 0.012687,
-     "end_time": "2026-10-05T11:06:26.359810+00:00",
+     "duration": 0.012129,
+     "end_time": "2026-10-20T06:27:59.321592+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.347123+00:00",
+     "start_time": "2026-10-20T06:27:59.309463+00:00",
      "status": "completed"
     },
     "tags": []
@@ -3192,16 +3228,16 @@
    "id": "exercise2-solution",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:26.387056Z",
-     "iopub.status.busy": "2026-10-05T11:06:26.386265Z",
-     "iopub.status.idle": "2026-10-05T11:06:26.398872Z",
-     "shell.execute_reply": "2026-10-05T11:06:26.397434Z"
+     "iopub.execute_input": "2026-10-20T06:27:59.344436Z",
+     "iopub.status.busy": "2026-10-20T06:27:59.344229Z",
+     "iopub.status.idle": "2026-10-20T06:27:59.350229Z",
+     "shell.execute_reply": "2026-10-20T06:27:59.349649Z"
     },
     "papermill": {
-     "duration": 0.029066,
-     "end_time": "2026-10-05T11:06:26.400610+00:00",
+     "duration": 0.017928,
+     "end_time": "2026-10-20T06:27:59.351024+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.371544+00:00",
+     "start_time": "2026-10-20T06:27:59.333096+00:00",
      "status": "completed"
     },
     "tags": []
@@ -3274,10 +3310,10 @@
    "id": "afc1e48a",
    "metadata": {
     "papermill": {
-     "duration": 0.012846,
-     "end_time": "2026-10-05T11:06:26.426048+00:00",
+     "duration": 0.00743,
+     "end_time": "2026-10-20T06:27:59.366508+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.413202+00:00",
+     "start_time": "2026-10-20T06:27:59.359078+00:00",
      "status": "completed"
     },
     "tags": []
@@ -3302,16 +3338,16 @@
    "id": "becb7d93",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:26.460239Z",
-     "iopub.status.busy": "2026-10-05T11:06:26.459575Z",
-     "iopub.status.idle": "2026-10-05T11:06:26.481188Z",
-     "shell.execute_reply": "2026-10-05T11:06:26.480098Z"
+     "iopub.execute_input": "2026-10-20T06:27:59.384873Z",
+     "iopub.status.busy": "2026-10-20T06:27:59.384618Z",
+     "iopub.status.idle": "2026-10-20T06:27:59.396330Z",
+     "shell.execute_reply": "2026-10-20T06:27:59.395752Z"
     },
     "papermill": {
-     "duration": 0.042019,
-     "end_time": "2026-10-05T11:06:26.483005+00:00",
+     "duration": 0.021927,
+     "end_time": "2026-10-20T06:27:59.396977+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.440986+00:00",
+     "start_time": "2026-10-20T06:27:59.375050+00:00",
      "status": "completed"
     },
     "tags": []
@@ -3393,16 +3429,16 @@
    "id": "6f576740",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:26.514843Z",
-     "iopub.status.busy": "2026-10-05T11:06:26.514180Z",
-     "iopub.status.idle": "2026-10-05T11:06:26.535085Z",
-     "shell.execute_reply": "2026-10-05T11:06:26.532984Z"
+     "iopub.execute_input": "2026-10-20T06:27:59.414469Z",
+     "iopub.status.busy": "2026-10-20T06:27:59.414228Z",
+     "iopub.status.idle": "2026-10-20T06:27:59.423878Z",
+     "shell.execute_reply": "2026-10-20T06:27:59.423234Z"
     },
     "papermill": {
-     "duration": 0.038946,
-     "end_time": "2026-10-05T11:06:26.537434+00:00",
+     "duration": 0.019731,
+     "end_time": "2026-10-20T06:27:59.424876+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.498488+00:00",
+     "start_time": "2026-10-20T06:27:59.405145+00:00",
      "status": "completed"
     },
     "tags": []
@@ -3496,10 +3532,10 @@
    "id": "c36f7294",
    "metadata": {
     "papermill": {
-     "duration": 0.014791,
-     "end_time": "2026-10-05T11:06:26.566456+00:00",
+     "duration": 0.007349,
+     "end_time": "2026-10-20T06:27:59.440589+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.551665+00:00",
+     "start_time": "2026-10-20T06:27:59.433240+00:00",
      "status": "completed"
     },
     "tags": []
@@ -3520,16 +3556,16 @@
    "id": "764c6b27",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:26.601393Z",
-     "iopub.status.busy": "2026-10-05T11:06:26.601137Z",
-     "iopub.status.idle": "2026-10-05T11:06:26.608471Z",
-     "shell.execute_reply": "2026-10-05T11:06:26.607952Z"
+     "iopub.execute_input": "2026-10-20T06:27:59.462124Z",
+     "iopub.status.busy": "2026-10-20T06:27:59.461707Z",
+     "iopub.status.idle": "2026-10-20T06:27:59.469845Z",
+     "shell.execute_reply": "2026-10-20T06:27:59.469037Z"
     },
     "papermill": {
-     "duration": 0.023816,
-     "end_time": "2026-10-05T11:06:26.609307+00:00",
+     "duration": 0.01921,
+     "end_time": "2026-10-20T06:27:59.470642+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.585491+00:00",
+     "start_time": "2026-10-20T06:27:59.451432+00:00",
      "status": "completed"
     },
     "tags": []
@@ -3540,13 +3576,7 @@
      "output_type": "stream",
      "text": [
       "Second temoin : Marseille -> Rennes\n",
-      "======================================================================\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "======================================================================\n",
       "          Heuristique                                                Chemin  Cout (km)  Noeuds explores\n",
       "       h (admissible) Marseille -> Toulouse -> Bordeaux -> Nantes -> Rennes       1100                8\n",
       "h x2 (non admissible)        Marseille -> Lyon -> Paris -> Nantes -> Rennes       1275                4\n",
@@ -3592,10 +3622,10 @@
    "id": "6ac45e53",
    "metadata": {
     "papermill": {
-     "duration": 0.007308,
-     "end_time": "2026-10-05T11:06:26.624327+00:00",
+     "duration": 0.016658,
+     "end_time": "2026-10-20T06:27:59.500965+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.617019+00:00",
+     "start_time": "2026-10-20T06:27:59.484307+00:00",
      "status": "completed"
     },
     "tags": []
@@ -3626,10 +3656,10 @@
    "id": "exercise3",
    "metadata": {
     "papermill": {
-     "duration": 0.007368,
-     "end_time": "2026-10-05T11:06:26.639294+00:00",
+     "duration": 0.013744,
+     "end_time": "2026-10-20T06:27:59.535741+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.631926+00:00",
+     "start_time": "2026-10-20T06:27:59.521997+00:00",
      "status": "completed"
     },
     "tags": []
@@ -3651,16 +3681,16 @@
    "id": "exercise3-solution",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:26.654932Z",
-     "iopub.status.busy": "2026-10-05T11:06:26.654683Z",
-     "iopub.status.idle": "2026-10-05T11:06:26.661695Z",
-     "shell.execute_reply": "2026-10-05T11:06:26.661064Z"
+     "iopub.execute_input": "2026-10-20T06:27:59.567019Z",
+     "iopub.status.busy": "2026-10-20T06:27:59.566543Z",
+     "iopub.status.idle": "2026-10-20T06:27:59.577232Z",
+     "shell.execute_reply": "2026-10-20T06:27:59.576216Z"
     },
     "papermill": {
-     "duration": 0.016202,
-     "end_time": "2026-10-05T11:06:26.662628+00:00",
+     "duration": 0.027113,
+     "end_time": "2026-10-20T06:27:59.578650+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.646426+00:00",
+     "start_time": "2026-10-20T06:27:59.551537+00:00",
      "status": "completed"
     },
     "tags": []
@@ -3672,8 +3702,8 @@
      "text": [
       "Heuristique MST pour le TSP (sur le graphe des villes françaises)\n",
       "=================================================================\n",
-      "  current=Bordeaux     unvisited={'Paris', 'Marseille', 'Lyon'}                → h=1224 km\n",
-      "  current=Paris        unvisited={'Marseille', 'Toulouse', 'Lyon', 'Nantes'}   → h=1527 km\n",
+      "  current=Bordeaux     unvisited={'Paris', 'Lyon', 'Marseille'}                → h=1224 km\n",
+      "  current=Paris        unvisited={'Toulouse', 'Nantes', 'Lyon', 'Marseille'}   → h=1527 km\n",
       "  current=Lyon         unvisited={'Grenoble', 'Nice'}                          → h=410 km\n",
       "\n",
       "Propriétés :\n",
@@ -3768,10 +3798,10 @@
    "id": "exercise4",
    "metadata": {
     "papermill": {
-     "duration": 0.00872,
-     "end_time": "2026-10-05T11:06:26.678763+00:00",
+     "duration": 0.015814,
+     "end_time": "2026-10-20T06:27:59.610435+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.670043+00:00",
+     "start_time": "2026-10-20T06:27:59.594621+00:00",
      "status": "completed"
     },
     "tags": []
@@ -3801,16 +3831,16 @@
    "id": "exercise4-solution",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:26.697223Z",
-     "iopub.status.busy": "2026-10-05T11:06:26.696813Z",
-     "iopub.status.idle": "2026-10-05T11:06:26.778637Z",
-     "shell.execute_reply": "2026-10-05T11:06:26.777672Z"
+     "iopub.execute_input": "2026-10-20T06:27:59.639659Z",
+     "iopub.status.busy": "2026-10-20T06:27:59.639347Z",
+     "iopub.status.idle": "2026-10-20T06:27:59.780170Z",
+     "shell.execute_reply": "2026-10-20T06:27:59.779182Z"
     },
     "papermill": {
-     "duration": 0.092712,
-     "end_time": "2026-10-05T11:06:26.779988+00:00",
+     "duration": 0.158053,
+     "end_time": "2026-10-20T06:27:59.780982+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.687276+00:00",
+     "start_time": "2026-10-20T06:27:59.622929+00:00",
      "status": "completed"
     },
     "tags": []
@@ -3830,7 +3860,7 @@
     },
     {
      "data": {
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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 660x520 with 1 Axes>"
       ]
@@ -3973,10 +4003,10 @@
    "id": "97bc629e",
    "metadata": {
     "papermill": {
-     "duration": 0.013982,
-     "end_time": "2026-10-05T11:06:26.805254+00:00",
+     "duration": 0.010662,
+     "end_time": "2026-10-20T06:27:59.804399+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.791272+00:00",
+     "start_time": "2026-10-20T06:27:59.793737+00:00",
      "status": "completed"
     },
     "tags": []
@@ -4009,16 +4039,16 @@
    "id": "2a6adc42",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-05T11:06:26.837215Z",
-     "iopub.status.busy": "2026-10-05T11:06:26.836831Z",
-     "iopub.status.idle": "2026-10-05T11:06:26.844361Z",
-     "shell.execute_reply": "2026-10-05T11:06:26.842698Z"
+     "iopub.execute_input": "2026-10-20T06:27:59.822333Z",
+     "iopub.status.busy": "2026-10-20T06:27:59.822042Z",
+     "iopub.status.idle": "2026-10-20T06:27:59.827270Z",
+     "shell.execute_reply": "2026-10-20T06:27:59.826581Z"
     },
     "papermill": {
-     "duration": 0.028243,
-     "end_time": "2026-10-05T11:06:26.846744+00:00",
+     "duration": 0.015064,
+     "end_time": "2026-10-20T06:27:59.827850+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.818501+00:00",
+     "start_time": "2026-10-20T06:27:59.812786+00:00",
      "status": "completed"
     },
     "tags": []
@@ -4108,10 +4138,10 @@
    "id": "conclusion-section",
    "metadata": {
     "papermill": {
-     "duration": 0.016566,
-     "end_time": "2026-10-05T11:06:26.877402+00:00",
+     "duration": 0.007979,
+     "end_time": "2026-10-20T06:27:59.843813+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.860836+00:00",
+     "start_time": "2026-10-20T06:27:59.835834+00:00",
      "status": "completed"
     },
     "tags": []
@@ -4161,10 +4191,10 @@
    "id": "nav-footer",
    "metadata": {
     "papermill": {
-     "duration": 0.014971,
-     "end_time": "2026-10-05T11:06:26.909269+00:00",
+     "duration": 0.008328,
+     "end_time": "2026-10-20T06:27:59.860533+00:00",
      "exception": false,
-     "start_time": "2026-10-05T11:06:26.894298+00:00",
+     "start_time": "2026-10-20T06:27:59.852205+00:00",
      "status": "completed"
     },
     "tags": []
@@ -4221,18 +4251,18 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.3"
+   "version": "3.13.7"
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 10.458291,
-   "end_time": "2026-10-05T11:06:27.377904+00:00",
+   "duration": 8.711474,
+   "end_time": "2026-10-20T06:28:00.333484+00:00",
    "environment_variables": {},
    "exception": null,
-   "input_path": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03-Informed.ipynb",
-   "output_path": "Search-03-Informed.ipynb",
+   "input_path": "C:\\dev\\CoursIA-2-c469-21226\\MyIA.AI.Notebooks\\Search\\Part1-Foundations\\Search-03-Informed.ipynb",
+   "output_path": "C:/dev/CoursIA-2-c469-21226/MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03-Informed.ipynb",
    "parameters": {},
-   "start_time": "2026-10-05T11:06:16.919613+00:00",
+   "start_time": "2026-10-20T06:27:51.622010+00:00",
    "version": "2.6.0"
   }
  },
diff --git a/scripts/notebook_tools/twin_pairs.d/search-03-informed.yaml b/scripts/notebook_tools/twin_pairs.d/search-03-informed.yaml
index 58830b6a48..2f6c88aebe 100644
--- a/scripts/notebook_tools/twin_pairs.d/search-03-informed.yaml
+++ b/scripts/notebook_tools/twin_pairs.d/search-03-informed.yaml
@@ -6,6 +6,8 @@
   bridge_verdict: SOTA-OK
   bridge_verdict_reason: "Python = stdlib + networkx (helper viz graph + heuristic lookup) + heapq (priority queue A*/Greedy/Best-First). Pas de lib SOTA-tier Python pour informed search pure (`aima-python` est jouet non maintained, `pySearch` est un essai), le port pedagogique AIMA ch.4 from-scratch reste canonique. C# = `QuikGraph` NuGet (Pelikan521, MIT-license, lib SOTA graphe .NET : `using QuikGraph;` + `using QuikGraph.Algorithms.ShortestPath;` + `DijkstraShortestPathAlgorithm<TVertex,TEdge>` / `AStarShortestPathAlgorithm<TVertex,TEdge>`, version 2.5.0). Verdict SOTA-OK (modèle owner PR #10412 MERGED pour Sudoku-09 / PR #10387 MERGED pour Search-02b NetworkX) : QuikGraph EST la lib SOTA graphe canonique .NET (Bellman-Ford, Dijkstra, A*, BFS, DFS -- tout y est), l'API `AStarShortestPathAlgorithm` permet un bridge direct A* avec heuristique. Asymetrie documentee : PY from-scratch (algos pedagogiques AIMA), C# bridge via lib SOTA (algo canonique)."
   known_differences:
+    - "Re-baseline 2026-10-21 (myia-po-2024:CoursIA-2, PR #21226, audit entry 0026) : edition UNILATERALE cote Python seul, dans la continuite de l'audit #21193. Deux cellules Python touchees depuis l'attestation 0024 (2026-10-18) : (a) cellule 56 (code, table de recapitulatif) -- la colonne `Complet` passe a `'Oui' if name in ['BFS','UCS','A*','Greedy'] else 'Non'`, alignement avec la table theorique c57 (`| Greedy (graph-search) | Oui*** |`) qui porte la note de portee (complet sur graphe fini avec successeurs finis ; NON complet en tree-search, peut boucler sur un cycle) ; (b) cellule 39 (markdown) -- prose recalee sur la sortie reelle de la cellule 40 (Frontiere max = 2, et non 4 qui comptait les bornes f explorees et non la profondeur recursive). Re-execution Python le 2026-10-20 (sorties et `execution_count` regeneres, papermill). Le jumeau C# est INTACT : 35 cellules, aucune modifiee, `csharp_sha` et `content_csharp_sha` identiques a l'audit 0024 (`492fb89e...` / `f5f735e9...`) ; le carnet C# ne contient aucune occurrence de `Complet` (verifie : 0 hit sur `Search-03-Informed-CSharp.ipynb`), la colonne n'existe pas cote C# -- l'asymetrie est donc STRUCTURELLE et non un defaut de parite, la contrepartie Python n'a pas d'equivalent a porter. Residu de precision assume : la table recapitulative c56 (construite en code) porte `Complet = Oui` sans le marqueur `***` ; la nuance (graphe fini vs tree-search) reste portee par la table theorique c57 et la section 3. Attestation = le blob SHA du jumeau Python se deplace (`d3296542...` -> `18be94ff...`, contenu `c511a953...` -> `98299fef...`) ; c'est une attestation de parite rafraichie, pas une reparation de contenu."
+    - "Re-baseline 2026-10-18 (myia-po-2024:CoursIA-2, #21226 -> audit #21193) : 2 jumeaux modifies sur correction d'un audit technique (Greedy graph-search complete + IDA* mesure profondeur reelle). Cote Python (cells 4, 9, 38, 57) : SearchResult ajoute champ `iterations` (kwarg, expose dans `display` si > 0) ; cellule 9 et 57 (md) corrigent la mention 'Non complet' du Greedy en distinguant tree-search vs graph-search avec closed set (Oui sur graphe fini, Non sur graphe infini) + note methodologique sur la portee ; cellule 38 (ida_star_search) suit la profondeur recursive reelle via un holder mutable et passe `max_recursion_depth` au 5eme slot de SearchResult (au lieu de `iterations` qui mesurait un compteur de bornes f, pas la memoire de pile reelle O(bd)). Cote C# (cell 21) : symetrique -- Dfs(node, curBound, depth) avec tracking `maxDepth` partage, passe `maxDepth` au 5eme slot. Verification Papermill Python : Frontiere max = 2 (profondeur recursive = 2 aretes) + Iterations = 4 (nb de bornes f), sorties coherentes. C# n'a pas le defaut 1 (tableau Greedy sans colonne 'Complet'). Issue #21193, PR #21226. Re-baseline des deux jumeaux."
     - 'Re-baseline 2026-10-05 (myia-po-2026:CoursIA, #19245 -> #19259) : edition unilaterale cote C# seul, COMMENTAIRES UNIQUEMENT -- la casse canonique `-CSharp` des noms de carnets jumeaux etait perdue dans les commentaires de code (`Search-02-Csharp` -> `Search-02-CSharp` (cellules 2, 4 et 19)). Aucune ligne executable touchee, aucun `execution_count` ni sortie modifie ; le cote Python est byte-identique a main. Parite semantique inchangee, seule la reference textuelle au jumeau redevient exacte : c''est une attestation de parite (le blob SHA du C# se deplace), pas une reparation. Re-baseline du csharp_sha.'
     - "Tranche #19143 (2026-10-04) : arc narratif du jumeau Python retravaille (enchainement des lectures de resultat ; markdown seul, aucun changement d'algorithme ni de sortie calculee), puis re-execute sous Python 3.11 pour annuler une derive de kernel (3.13 -> 3.11, `Kernel drift guard base vs PR`). Le jumeau C# est intact : `csharp_sha` identique entre les audits 0016 et 0017."
     - "Socle pedagogique commun : A* et Greedy Best-First avec differentes heuristiques (distance Manhattan, euclidienne, h=0=degenere en UCS). Arrive en Roumanie + 8-puzzle."
diff --git a/scripts/notebook_tools/twin_pairs.d/search-03-informed/0024-2026-10-18-myia-po-2024-CoursIA-2.yaml b/scripts/notebook_tools/twin_pairs.d/search-03-informed/0024-2026-10-18-myia-po-2024-CoursIA-2.yaml
new file mode 100644
index 0000000000..f581984118
--- /dev/null
+++ b/scripts/notebook_tools/twin_pairs.d/search-03-informed/0024-2026-10-18-myia-po-2024-CoursIA-2.yaml
@@ -0,0 +1,6 @@
+date: '2026-10-18'
+by: myia-po-2024:CoursIA-2
+python_sha: d329654242c8402ee5e29141e7de0dec618ed5f5
+csharp_sha: 492fb89e7e1ac7ab0f4f8cba9d1fc30863edb0f6
+content_python_sha: c511a953e42c0d5d4a8d684b66f09a5f1062334a5fbf4f92b5162fa4dab28aae
+content_csharp_sha: f5f735e94ef8bc3b7d1eee6cb6560bb3df7d3ffdd4c59deae9ff29886970a244
diff --git a/scripts/notebook_tools/twin_pairs.d/search-03-informed/0026-2026-10-21-myia-po-2024-CoursIA-2.yaml b/scripts/notebook_tools/twin_pairs.d/search-03-informed/0026-2026-10-21-myia-po-2024-CoursIA-2.yaml
new file mode 100644
index 0000000000..7785059f6a
--- /dev/null
+++ b/scripts/notebook_tools/twin_pairs.d/search-03-informed/0026-2026-10-21-myia-po-2024-CoursIA-2.yaml
@@ -0,0 +1,6 @@
+date: '2026-10-21'
+by: myia-po-2024:CoursIA-2
+python_sha: 18be94ff2a29df8365105bf509e6931cd6eb3cbf
+csharp_sha: 492fb89e7e1ac7ab0f4f8cba9d1fc30863edb0f6
+content_python_sha: 98299fef6ca7682c2ab6747d688ca8bba391f90d0d74d5fab1b93e1e0d48947e
+content_csharp_sha: f5f735e94ef8bc3b7d1eee6cb6560bb3df7d3ffdd4c59deae9ff29886970a244