diff --git a/Week 4/8 Eigenvalues and Eigenvectors.ipynb b/Week 4/8 Eigenvalues and Eigenvectors.ipynb
index d810302..ef17fe3 100644
--- a/Week 4/8 Eigenvalues and Eigenvectors.ipynb	
+++ b/Week 4/8 Eigenvalues and Eigenvectors.ipynb	
@@ -39,7 +39,7 @@
    "cell_type": "raw",
    "metadata": {},
    "source": [
-    "team_members = \"\""
+    "team_members = \"Koen Vendrig, Kees van Kempen\""
    ]
   },
   {
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -83,10 +83,8 @@
    },
    "outputs": [],
    "source": [
-    "# Import packages here ...\n",
-    "\n",
-    "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "import numpy as np\n",
+    "import numpy.linalg as linalg"
    ]
   },
   {
@@ -139,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -157,13 +155,53 @@
    "outputs": [],
    "source": [
     "def inversePower(A, sigma, eps):\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()"
+    "    \"\"\"\n",
+    "    Estimates the eigenvectors of matrix A by the inverse power method.\n",
+    "\n",
+    "    Args:\n",
+    "        A: an n x n matrix\n",
+    "        sigma: an initial guess for an eigenvector\n",
+    "        eps: the desired accuracy\n",
+    "\n",
+    "    Returns:\n",
+    "        A tuple (b, k) is returned, containing:\n",
+    "            b: the eigenvector b corresponding to the eigenvalue\n",
+    "               closests to sigma after k iterations\n",
+    "            k: the number of iterations done\n",
+    "            \n",
+    "    See also:\n",
+    "        https://www.sciencedirect.com/topics/mathematics/inverse-power-method\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Does https://johnfoster.pge.utexas.edu/numerical-methods-book/LinearAlgebra_EigenProblem1.html help?\n",
+    "    \n",
+    "    # A should be n x n.\n",
+    "    n = len(A)\n",
+    "    assert len(A.shape) == 2 and A.shape[0] == A.shape[1]\n",
+    "    \n",
+    "    # Setup some initial values.\n",
+    "    #B = linalg.inv(A - sigma*np.ones(n))\n",
+    "    B = linalg.inv(A - sigma*np.eye(n))\n",
+    "    #B = 1/(A - sigma*np.ones(n))\n",
+    "    #B = 1/(A - sigma*np.eye(n))\n",
+    "    b = np.ones(n)\n",
+    "    k = 0\n",
+    "    e = 0\n",
+    "    \n",
+    "    while e > eps or k == 0:\n",
+    "        b_prev = b.copy()\n",
+    "        k += 1\n",
+    "        \n",
+    "        b = B @ b\n",
+    "        b /= np.sqrt(b @ b) # although b = linalg.norm(b) could be used\n",
+    "        e = np.sqrt(np.sum( (np.abs(b_prev) - np.abs(b))**2) )\n",
+    "    \n",
+    "    return b, k"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -178,12 +216,48 @@
      "task": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For sigma = 0.03\n",
+      "b = [ 0.45440139 -0.76618454  0.45440139]\n",
+      "lam = [0.62771919 0.62771831 0.62771919]\n",
+      "\n",
+      "\n",
+      "For sigma = 6.0\n",
+      "b = [0.54177432 0.64262054 0.54177432]\n",
+      "lam = [6.37228128 6.37228139 6.37228128]\n",
+      "\n",
+      "\n",
+      "For sigma = 1.99999989999999\n",
+      "b = [-7.07106781e-01 -4.21799612e-14  7.07106781e-01]\n",
+      "lam = [ 2. -0.  2.]\n",
+      "\n",
+      "\n",
+      "[6.37228132 2.         0.62771868]\n"
+     ]
+    }
+   ],
    "source": [
     "# Use this cell for your own testing ...\n",
     "\n",
-    "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "A = np.array([[3, 2, 1], [2, 3, 2], [1, 2, 3]])\n",
+    "\n",
+    "sigma_list = [.03, 6., 1.99999989999999]\n",
+    "#sigma = 0.03\n",
+    "\n",
+    "for sigma in sigma_list:\n",
+    "    print(\"For sigma =\", sigma)\n",
+    "    b, k = inversePower(A, sigma, 1e-6)\n",
+    "    print(\"b =\", b)\n",
+    "    lam = np.dot(A, b) / b\n",
+    "    print(\"lam =\", lam)\n",
+    "    print()\n",
+    "    print()\n",
+    "\n",
+    "print(linalg.eig(A)[0])"
    ]
   },
   {
@@ -206,6 +280,7 @@
    "outputs": [],
    "source": [
     "def test_inversePower():\n",
+    "    A = np.array([[3, 2, 1], [2, 3, 2], [1, 2, 3]])\n",
     "    # YOUR CODE HERE\n",
     "    raise NotImplementedError()\n",
     "        \n",
@@ -506,7 +581,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },