diff --git a/Schenk_Brandenberger_S10_Aufg1.pdf b/Schenk_Brandenberger_S10_Aufg1.pdf
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diff --git a/Schenk_Brandenberger_S10_Aufg1.py b/Schenk_Brandenberger_S10_Aufg1.py
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+import numpy as np
+
+
+print("*********Jacobi-Verfahren*********")
+def Jacobi(A,b,x,steps):
+    L = np.tril(A,k=-1)
+    D = np.diag(np.diag(A))
+    R = np.triu(A, k=1)
+    for steps in range(steps):
+        x = -np.linalg.inv(D) @ (L + R) @ x + np.linalg.inv(D) @ b
+    #print(np.linalg.norm((-np.linalg.inv(D) @ (L + R)),np.inf))
+    return x
+
+
+
+A = np.array([[8,5,2],[5,9,1],[4,2,7]])
+b = np.array([[19],[5],[34]])
+x = np.array([[1],[-1],[3]])
+steps = 3
+print(Jacobi(A,b,x,steps))
+print("Sum of vector:")
+#print(np.sum(Jacobi(A,b,x,steps)))
+#print(np.linalg.norm(Jacobi(A,b,x,3)-Jacobi(A,b,x,2),np.inf)*7)
+print(np.linalg.norm(Jacobi(A,b,x,3)-Jacobi(A,b,x,2),np.inf))
+print((0.0001*0.125)/0.7693452380952384)
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