diff --git a/sheets/02_cpsat/DBST/_solver.py b/sheets/02_cpsat/DBST/_solver.py
index 5612b648eb8a783f45855dcf2d57dd61fb4db5a0..13b8a45c201f34b6ef07f79632df77163d47b259 100644
--- a/sheets/02_cpsat/DBST/_solver.py
+++ b/sheets/02_cpsat/DBST/_solver.py
@@ -1,87 +1,96 @@
 from ortools.sat.python import cp_model
 import itertools
 
+def squared_distance(p1, p2):
+    """
+    Calculate the squared euclidian distance (in order to minimze the use of the sqrt() operation).
+    """
+    return (p1[0]-p2[0])**2 + (p1[1]-p2[1])**2
+
 class DBSTSolverCP:
-    def __squared_distance(self, vi, wi):
-        """
-        Gegeben zwei Punkt-Indizes, berechne die (ganzzahlige)
-        quadrierte Distanz zwischen den Punkten.
-        """
-        v, w = self.points[vi], self.points[wi]
-        return (v[0] - w[0])**2 + (v[1] - w[1])**2
-    
-    def __compute_distances(self):
+    def __calculate_distances(self) -> int:
         """
-        Berechne eine Matrix der Distanzen.
+        Precalculate the costs of all edges. The side effect of this method
+        is the calculated max_distance, which is necessary in order to specify
+        our bottleneck variable's upper bound.
         """
-        self.distances = {(v,w): self.__squared_distance(v, w) for v,w in\
-                          itertools.product(range(self.n), range(self.n))}
+        self.distances = {(i, j): squared_distance(self.points[i], self.points[j]) 
+            for (i, j) in itertools.permutations(range(len(self.points)), 2)}
         self.max_distance = max(self.distances.values())
     
     def __make_vars(self):
         """
-        Erzeuge die Variablen und setze die Zielfunktion.
+        Create all involved variables and set the minimization objective.
         """
-        self.edge_vars = {(v,w): self.model.NewBoolVar(f'x_{v},{w}') for v,w in itertools.combinations(range(self.n), 2)}
-        self.edge_vars.update({(w,v): self.model.NewBoolVar(f'x_{w},{v}') for v,w in self.edge_vars})
+        self.edge_vars = {(v,w): self.model.NewBoolVar(f'x_{v},{w}') 
+                            for v,w in itertools.permutations(range(self.n), 2)}
         self.depth_vars = {v: self.model.NewIntVar(0, self.n-1, f'd_{v}') for v in range(self.n)}
         self.bottleneck_var = self.model.NewIntVar(0, self.max_distance, 'b')
         self.model.Minimize(self.bottleneck_var)
     
+    def __add_depth_constraints(self):
+        """
+        Add the depth constraints x_{v,w} -> d_w = d_v + 1 which guarantee the
+        validity of the arborescence.
+        """
+
+        # without loss of generality, force one node to be the root.
+        self.model.Add(self.depth_vars[0] == 0)
+
+        # spanning tree has exactly n-1 edges.
+        self.model.Add(sum(self.edge_vars.values()) == self.n-1)
+
+        # If the edge v -> w is selected, d(v) + 1 == d(w) must hold.
+        for (v, w), x_vw in self.edge_vars.items():
+            self.model.Add(self.depth_vars[w] == self.depth_vars[v] + 1).OnlyEnforceIf(x_vw)
+
     def __add_degree_constraints(self):
         """
-        Füge die Gradbedingungen hinzu.
+        Add an upper limit to the degree of every node.
         """
+
+        # Handle the root node: No edge must lead to the root + enforce degree constraint.
         v0out = 0
         for v in range(1, self.n):
             self.model.Add(self.edge_vars[v,0] == 0)  # x_vv0 = 0
             v0out += self.edge_vars[0,v]
-        self.model.Add(v0out <= self.max_degree)  # Grad d für v0
+        self.model.Add(v0out <= self.max_degree)
+
+        # Handle all other nodes.
         for v in range(1, self.n):
+            # "Count" the number of incoming and outgoing edges.
             vin, vout = 0, 0
             for w in range(0, self.n):
                 if v != w:
                     vin += self.edge_vars[w,v]
                     vout += self.edge_vars[v,w]
-            self.model.Add(vin == 1)  # genau 1 eingehende Kante für v
-            self.model.Add(vout <= self.max_degree - 1)  # <= d-1 ausgehende Kanten für v
+            self.model.Add(vin == 1)  # exactly one incoming edge
+            self.model.Add(vout <= self.max_degree - 1)  # <= at most k-1 outgoing edges
     
     def __forbid_bidirectional_edges(self):
         """
-        Füge die (streng genommen redundanten) Constraints x_{v,w} -> !x_{w, v} hinzu.
+        Add the (redundant) constraints x_{v,w} -> !x_{w, v}.
         """
         for v,w in itertools.combinations(range(self.n), 2):
             self.model.AddBoolOr([self.edge_vars[v,w].Not(), self.edge_vars[w,v].Not()])
-    
-    def __add_depth_constraints(self):
-        """
-        Füge die Tiefenconstraints x_{v,w} -> d_w = d_v + 1 hinzu.
-        """
-        self.model.Add(self.depth_vars[0] == 0)
-        all_edges = 0
-        for (v, w), x_vw in self.edge_vars.items():
-            self.model.Add(self.depth_vars[w] == self.depth_vars[v] + 1).OnlyEnforceIf(x_vw)
-            all_edges += x_vw
-        self.model.Add(all_edges == self.n-1)
         
     def __add_bottleneck_constraints(self):
         """
-        Füge die Constraints b >= d(v,w) * x_{v,w} hinzu.
+        Add the constraints to ensure that only edges as cheap or cheaper than 
+        the bottleneck can be selected (b >= d(v,w) * x_{v,w}).
         """
         for (v, w), x_vw in self.edge_vars.items():
             self.model.Add(self.bottleneck_var >= self.distances[v,w] * x_vw)
     
     def __init__(self, points, max_degree):
         """
-        Erzeuge das Modell.
-        :param points: Liste der Punkte (ganzzahlig, in der Ebene, als (x,y)-Tupel).
-        :param max_degree: Der höchste zulässige Grad in unserem Spannbaum.
+        Initialize the model.
         """
         self.points = points
         self.n = len(self.points)
         self.max_degree = max_degree
         self.model = cp_model.CpModel()
-        self.__compute_distances()
+        self.__calculate_distances()
         self.__make_vars()
         self.__forbid_bidirectional_edges()
         self.__add_degree_constraints()
@@ -90,11 +99,13 @@ class DBSTSolverCP:
         
     def solve(self):
         """
-        Suche die optimale Lösung für das konstruierte Modell und gebe eine Lösung
-        (Liste von Kanten als ((x1,y1),(x2,y2))-Tupel) zurück.
+        Find the optimal solution to the initialized instance.
+        Returns the DBST edges as a list of coordinate tuple tuples ((x1,y1),(x2,y2)).
         """
         solver = cp_model.CpSolver()
         status = solver.Solve(self.model)
+        if status == cp_model.INFEASIBLE:
+            raise RuntimeError("The model was classified infeasible by the solver!")
         if status != cp_model.OPTIMAL:
             raise RuntimeError("Unexpected status after running solver!")
         return [(self.points[v], self.points[w]) for (v,w),x_vw in self.edge_vars.items() if solver.Value(x_vw) != 0]
\ No newline at end of file
diff --git a/sheets/02_cpsat/DBST/constraint_programming.ipynb b/sheets/02_cpsat/DBST/constraint_programming.ipynb
index 0e698503b4e22510d33e542cb31fb32b15c632e4..20e332d2e52e8c58092f46bf42b48cbc6f0ee401 100644
--- a/sheets/02_cpsat/DBST/constraint_programming.ipynb
+++ b/sheets/02_cpsat/DBST/constraint_programming.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 22,
    "id": "62e4c444-c034-4183-b096-a51f3d0223c7",
    "metadata": {},
    "outputs": [],
@@ -22,7 +22,7 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "# our code\n",
-    "from _solver import DBSTSolverCP"
+    "from _solver import squared_distance, DBSTSolverCP"
    ]
   },
   {
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 23,
    "id": "098ad290-5284-4e19-bbf3-dd3d240561ab",
    "metadata": {},
    "outputs": [],
@@ -63,12 +63,6 @@
     "    \"\"\"\n",
     "    return [(random.randint(0,w), random.randint(0,h)) for _ in range(n)]\n",
     "\n",
-    "def squared_distance(p1, p2):\n",
-    "    \"\"\"\n",
-    "    Calculate the squared euclidian distance (in order to minimze the use of the sqrt() operation).\n",
-    "    \"\"\"\n",
-    "    return (p1[0]-p2[0])**2 + (p1[1]-p2[1])**2\n",
-    "\n",
     "def draw_dbst_edges(edges):\n",
     "    \"\"\"\n",
     "    Draw the edges of a DBST. The bottleneck edge(s) automatically get highlighted.\n",
@@ -100,13 +94,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 24,
    "id": "10a54359-24a9-41ff-872f-c0b48a29e4bb",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -117,7 +111,7 @@
    ],
    "source": [
     "random.seed(1234567) # remove if you want random instances\n",
-    "solver = DBSTSolverCP(random_points(50), 3)\n",
+    "solver = DBSTSolverCP(random_points(40), 3)\n",
     "draw_dbst_edges(solver.solve())"
    ]
   }