diff --git a/README.md b/README.md
index fe9b6d2..f8ca7ed 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@ A python module for the synthetic control method that provides implementations o
 - Synthetic Control Method (Abadie & Gardeazabal 2003)
 - Robust Synthetic Control Method (Amjad, Shah & Shen 2018)
 - Augmented Synthetic Control Method (Ben-Michael, Feller & Rothstein 2021)
-- ~~Penalized Synthetic Control Method (Abadie & L'Hour 2021)~~ (the implementation of this method is flawed, and in the process of being corrected.)
+- Penalized Synthetic Control Method (Abadie & L'Hour 2021)
 
 The package also provides methods for performing placebo tests with the above.
 
diff --git a/examples/penalized/basque_penalized.ipynb b/examples/penalized/basque_penalized.ipynb
index 21adcf8..e485289 100644
--- a/examples/penalized/basque_penalized.ipynb
+++ b/examples/penalized/basque_penalized.ipynb
@@ -7,7 +7,7 @@
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "from pysyncon import Dataprep, PenalizedSynth"
+    "from pysyncon import Dataprep, PenalizedSynth, Synth"
    ]
   },
   {
@@ -79,17 +79,17 @@
      "data": {
       "text/plain": [
        "Aragon                          0.000\n",
-       "Baleares (Islas)                0.217\n",
+       "Baleares (Islas)                0.000\n",
        "Andalucia                       0.000\n",
        "Canarias                        0.000\n",
-       "Cantabria                       0.000\n",
+       "Cantabria                       0.241\n",
        "Castilla Y Leon                 0.000\n",
        "Castilla-La Mancha              0.000\n",
-       "Cataluna                        0.637\n",
+       "Cataluna                        0.759\n",
        "Comunidad Valenciana            0.000\n",
        "Extremadura                     0.000\n",
        "Galicia                         0.000\n",
-       "Madrid (Comunidad De)           0.147\n",
+       "Madrid (Comunidad De)           0.000\n",
        "Murcia (Region de)              0.000\n",
        "Navarra (Comunidad Foral De)    0.000\n",
        "Principado De Asturias          0.000\n",
@@ -114,7 +114,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -134,7 +134,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -182,85 +182,85 @@
        "    <tr>\n",
        "      <th>school.illit</th>\n",
        "      <td>39.888</td>\n",
-       "      <td>206.835</td>\n",
+       "      <td>213.304</td>\n",
        "      <td>323.826</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>school.prim</th>\n",
        "      <td>1031.742</td>\n",
-       "      <td>2172.883</td>\n",
+       "      <td>2259.100</td>\n",
        "      <td>2182.453</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>school.med</th>\n",
        "      <td>90.359</td>\n",
-       "      <td>180.914</td>\n",
+       "      <td>169.182</td>\n",
        "      <td>148.864</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>school.high</th>\n",
        "      <td>25.728</td>\n",
-       "      <td>50.936</td>\n",
+       "      <td>50.057</td>\n",
        "      <td>47.133</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>school.post.high</th>\n",
        "      <td>13.480</td>\n",
-       "      <td>29.872</td>\n",
+       "      <td>25.277</td>\n",
        "      <td>26.163</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>invest</th>\n",
        "      <td>24.647</td>\n",
-       "      <td>22.015</td>\n",
+       "      <td>24.097</td>\n",
        "      <td>21.445</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>special.1.gdpcap</th>\n",
        "      <td>5.285</td>\n",
-       "      <td>5.284</td>\n",
+       "      <td>4.840</td>\n",
        "      <td>3.584</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>special.2.sec.agriculture</th>\n",
        "      <td>6.844</td>\n",
-       "      <td>7.534</td>\n",
+       "      <td>9.099</td>\n",
        "      <td>21.058</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>special.3.sec.energy</th>\n",
        "      <td>4.106</td>\n",
-       "      <td>2.587</td>\n",
+       "      <td>2.883</td>\n",
        "      <td>5.252</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>special.4.sec.industry</th>\n",
        "      <td>45.082</td>\n",
-       "      <td>32.951</td>\n",
+       "      <td>39.207</td>\n",
        "      <td>22.670</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>special.5.sec.construction</th>\n",
        "      <td>6.150</td>\n",
-       "      <td>7.293</td>\n",
+       "      <td>6.530</td>\n",
        "      <td>7.274</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>special.6.sec.services.venta</th>\n",
        "      <td>33.754</td>\n",
-       "      <td>43.819</td>\n",
+       "      <td>37.724</td>\n",
        "      <td>36.646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>special.7.sec.services.nonventa</th>\n",
        "      <td>4.072</td>\n",
-       "      <td>5.818</td>\n",
+       "      <td>4.559</td>\n",
        "      <td>7.103</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>special.8.popdens</th>\n",
        "      <td>246.890</td>\n",
-       "      <td>184.961</td>\n",
+       "      <td>137.300</td>\n",
        "      <td>97.468</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -269,20 +269,20 @@
       ],
       "text/plain": [
        "                                  treated  synthetic  sample mean\n",
-       "school.illit                       39.888    206.835      323.826\n",
-       "school.prim                      1031.742   2172.883     2182.453\n",
-       "school.med                         90.359    180.914      148.864\n",
-       "school.high                        25.728     50.936       47.133\n",
-       "school.post.high                   13.480     29.872       26.163\n",
-       "invest                             24.647     22.015       21.445\n",
-       "special.1.gdpcap                    5.285      5.284        3.584\n",
-       "special.2.sec.agriculture           6.844      7.534       21.058\n",
-       "special.3.sec.energy                4.106      2.587        5.252\n",
-       "special.4.sec.industry             45.082     32.951       22.670\n",
-       "special.5.sec.construction          6.150      7.293        7.274\n",
-       "special.6.sec.services.venta       33.754     43.819       36.646\n",
-       "special.7.sec.services.nonventa     4.072      5.818        7.103\n",
-       "special.8.popdens                 246.890    184.961       97.468"
+       "school.illit                       39.888    213.304      323.826\n",
+       "school.prim                      1031.742   2259.100     2182.453\n",
+       "school.med                         90.359    169.182      148.864\n",
+       "school.high                        25.728     50.057       47.133\n",
+       "school.post.high                   13.480     25.277       26.163\n",
+       "invest                             24.647     24.097       21.445\n",
+       "special.1.gdpcap                    5.285      4.840        3.584\n",
+       "special.2.sec.agriculture           6.844      9.099       21.058\n",
+       "special.3.sec.energy                4.106      2.883        5.252\n",
+       "special.4.sec.industry             45.082     39.207       22.670\n",
+       "special.5.sec.construction          6.150      6.530        7.274\n",
+       "special.6.sec.services.venta       33.754     37.724       36.646\n",
+       "special.7.sec.services.nonventa     4.072      4.559        7.103\n",
+       "special.8.popdens                 246.890    137.300       97.468"
       ]
      },
      "execution_count": 6,
@@ -293,6 +293,54 @@
    "source": [
     "pen.summary()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This method will recover the weights from the traditional synthetic control method if we set $\\lambda = 0$ and use the appropriate $V$ matrix ($\\Gamma$ matrix in the notation of the Abadie and L'Hour paper)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Aragon                          0.000\n",
+       "Baleares (Islas)                0.000\n",
+       "Andalucia                       0.000\n",
+       "Canarias                        0.000\n",
+       "Cantabria                       0.000\n",
+       "Castilla Y Leon                 0.000\n",
+       "Castilla-La Mancha              0.000\n",
+       "Cataluna                        0.851\n",
+       "Comunidad Valenciana            0.000\n",
+       "Extremadura                     0.000\n",
+       "Galicia                         0.000\n",
+       "Madrid (Comunidad De)           0.149\n",
+       "Murcia (Region de)              0.000\n",
+       "Navarra (Comunidad Foral De)    0.000\n",
+       "Principado De Asturias          0.000\n",
+       "Rioja (La)                      0.000\n",
+       "Spain (Espana)                  0.000\n",
+       "Name: weights, dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "synth = Synth()\n",
+    "synth.fit(dataprep=dataprep)\n",
+    "\n",
+    "pen.fit(dataprep=dataprep, lambda_=0.0, custom_V=synth.V)\n",
+    "pen.weights()"
+   ]
   }
  ],
  "metadata": {
diff --git a/pysyncon/penalized.py b/pysyncon/penalized.py
index c5347ae..bfecdd5 100644
--- a/pysyncon/penalized.py
+++ b/pysyncon/penalized.py
@@ -1,7 +1,8 @@
 from __future__ import annotations
-from typing import Optional
+from typing import Optional, Literal, Union
 
 import numpy as np
+import pandas as pd
 from scipy.optimize import minimize, Bounds, LinearConstraint
 
 from .dataprep import Dataprep
@@ -11,24 +12,33 @@
 class PenalizedOptimMixin:
     @staticmethod
     def w_optimize(
+        V_mat: np.ndarray,
         X0: np.ndarray,
         X1: np.ndarray,
         lambda_: float,
-        initial: Optional[np.ndarray] = None,
+        qp_method: Literal["SLSQP"] = "SLSQP",
+        qp_options: dict = {"maxiter": 1000},
     ) -> tuple[np.ndarray, float]:
         """Solves the weight optimisation problem in the penalized setting.
 
         Parameters
         ----------
-        X0 : numpy.ndarray, shape (m, c)
+        V_mat : numpy.ndarray, shape (c, c)
+            The V matrix (using the notation of the Abadie, Diamond &
+            Hainmueller paper, this matrix is denoted by Γ in the Abadie and
+            L'Hour paper).
+        X0 : numpy.ndarray, shape (c, m)
             Matrix with each column corresponding to a control unit and each
             row is covariates.
-        X1 : numpy.ndarray, shape (m,)
+        X1 : numpy.ndarray, shape (c,)
             Column vector giving the covariate values for the treated unit.
         lambda_ : float,
             Regularization parameter.
-        initial: numpy.ndarray, shape(m,), optional
-            Initial point to use in the optimisation problem.
+        qp_method : str, optional
+            Minimization routine to use in scipy minimize to solve the problem
+            , by default "SLSQP"
+        qp_options : dict, optional
+            Options for scipy minimize, by default {"maxiter": 1000}
 
         Returns
         -------
@@ -38,21 +48,28 @@ def w_optimize(
         :meta private:
         """
         _, n_c = X0.shape
+
         diff = np.subtract(X0, X1.reshape(-1, 1))
-        r = np.linalg.norm(diff, axis=0)
+        r = np.diag(diff.T @ V_mat @ diff)
+
+        P = X0.T @ V_mat @ X0
+        q = -1.0 * X1.T @ V_mat @ X0 + (lambda_ / 2.0) * r.T
 
         def fun(x):
-            return (X1 - X0 @ x).T @ (X1 - X0 @ x) + lambda_ * (r.T @ x)
+            return q.T @ x + 0.5 * x.T @ P @ x
 
         bounds = Bounds(lb=np.full(n_c, 0.0), ub=np.full(n_c, 1.0))
         constraints = LinearConstraint(A=np.full(n_c, 1.0), lb=1.0, ub=1.0)
 
-        if initial:
-            x0 = initial
-        else:
-            x0 = np.full(n_c, 1 / n_c)
-
-        res = minimize(fun=fun, x0=x0, bounds=bounds, constraints=constraints)
+        x0 = np.full(n_c, 1 / n_c)
+        res = minimize(
+            fun=fun,
+            x0=x0,
+            bounds=bounds,
+            constraints=constraints,
+            method=qp_method,
+            options=qp_options,
+        )
         W, loss_W = res["x"], res["fun"]
         return W, loss_W.item()
 
@@ -64,24 +81,67 @@ class PenalizedSynth(BaseSynth, PenalizedOptimMixin):
 
     def __init__(self) -> None:
         super().__init__()
-        self.W: Optional[np.ndarray] = None
+        self.loss_W: Optional[float] = None
         self.lambda_: Optional[float] = None
 
-    def fit(self, dataprep: Dataprep, lambda_: float) -> None:
+    def fit(
+        self,
+        dataprep: Optional[Dataprep] = None,
+        X0: Optional[pd.DataFrame] = None,
+        X1: Optional[Union[pd.Series, pd.DataFrame]] = None,
+        lambda_: Optional[float] = 0.01,
+        custom_V: Optional[np.ndarray] = None,
+    ) -> None:
         """Fit the model/calculate the weights.
 
         Parameters
         ----------
-        dataprep : Dataprep
-            :class:`Dataprep` object containing data to model.
-        lambda_ : float
-            Ridge parameter to use.
+        dataprep : Dataprep, optional
+            :class:`Dataprep` object containing data to model, by default None.
+        X0 : pd.DataFrame, shape (c, m), optional
+            Matrix with each column corresponding to a control unit and each
+            row is a covariate value, by default None.
+        X1 : pandas.Series | pandas.DataFrame, shape (c, 1), optional
+            Column vector giving the covariate values for the treated unit, by
+            default None.
+        lambda_ : float, optional
+            Ridge parameter to use, default 0.01
+        custom_V : numpy.ndarray, shape (c, c), optional
+            Provide a V matrix (using the notation of the Abadie, Diamond &
+            Hainmueller paper, this matrix is denoted by Γ in the Abadie and
+            L'Hour paper), if not provided then the identity matrix is used
+            (equal importance to all covariates).
+
+        Returns
+        -------
+        NoneType
+            None
+
+        Raises
+        ------
+        ValueError
+            if neither a Dataprep object nor all of (X0, X1) are
+            supplied.
         """
-        self.dataprep = dataprep
+        if dataprep:
+            self.dataprep = dataprep
+            X0, X1 = dataprep.make_covariate_mats()
+        else:
+            if X0 is None or X1 is None:
+                raise ValueError("dataprep must be set or (X0, X1) must all be set.")
         self.lambda_ = lambda_
 
-        X0_df, X1_df = dataprep.make_outcome_mats()
-        X0, X1 = X0_df.to_numpy(), X1_df.to_numpy()
+        X = pd.concat([X0, X1], axis=1)
+        X_scaled = X.divide(X.std(axis=1), axis=0)
+        X0_scaled, X1_scaled = X_scaled.drop(columns=X1.name), X_scaled[X1.name]
+
+        X0_arr = X0_scaled.to_numpy()
+        X1_arr = X1_scaled.to_numpy()
+
+        if custom_V is None:
+            V_mat = np.identity(X0_arr.shape[0])
+        else:
+            V_mat = np.diag(custom_V)
 
-        W, _ = self.w_optimize(X0=X0, X1=X1, lambda_=lambda_)
-        self.W = W
+        W, loss_W = self.w_optimize(V_mat=V_mat, X0=X0_arr, X1=X1_arr, lambda_=lambda_)
+        self.W, self.loss_W = W, loss_W
diff --git a/tests/test_penalized.py b/tests/test_penalized.py
new file mode 100644
index 0000000..0ca114c
--- /dev/null
+++ b/tests/test_penalized.py
@@ -0,0 +1,43 @@
+import unittest
+import numpy as np
+import pandas as pd
+
+import pysyncon
+
+
+class TestPenalizedSynth(unittest.TestCase):
+    def setUp(self):
+        self.dataprep = pysyncon.Dataprep(
+            foo=pd.DataFrame(
+                {
+                    "time": [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4],
+                    "name": [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],
+                    "dependent": np.random.random(12),
+                    "predictor1": np.random.random(12),
+                    "predictor2": np.random.random(12),
+                }
+            ),
+            predictors=["predictor1"],
+            predictors_op="mean",
+            dependent="dependent",
+            unit_variable="name",
+            time_variable="time",
+            treatment_identifier=1,
+            controls_identifier=[2, 3],
+            time_predictors_prior=[2, 3],
+            time_optimize_ssr=[1, 2, 3],
+            special_predictors=[
+                ("predictor1", [2], "mean"),
+                ("predictor2", [1, 2], "median"),
+                ("predictor2", [1, 2], "std"),
+            ],
+        )
+        self.custom_V = np.full(4, 1.0)
+
+    def test_fit_no_data(self):
+        pen = pysyncon.PenalizedSynth()
+        self.assertRaises(ValueError, pen.fit)
+
+    def test_fit_custom_V(self):
+        pen = pysyncon.PenalizedSynth()
+        pen.fit(dataprep=self.dataprep, custom_V=self.custom_V)
diff --git a/tests/test_penalized_basque.py b/tests/test_penalized_basque.py
index 93ddb03..86b1a01 100644
--- a/tests/test_penalized_basque.py
+++ b/tests/test_penalized_basque.py
@@ -50,27 +50,29 @@ def setUp(self):
                 "Navarra (Comunidad Foral De)",
                 "Principado De Asturias",
                 "Rioja (La)",
+                "Spain (Espana)",
             ],
             time_optimize_ssr=range(1960, 1970),
         )
         self.lambda_ = 0.01
         self.weights = {
             "Aragon": 0.0,
-            "Baleares (Islas)": 0.21680004,
+            "Baleares (Islas)": 0.0,
             "Andalucia": 0.0,
             "Canarias": 0.0,
-            "Cantabria": 0.0,
+            "Cantabria": 0.241,
             "Castilla Y Leon": 0.0,
             "Castilla-La Mancha": 0.0,
-            "Cataluna": 0.636479454,
+            "Cataluna": 0.759,
             "Comunidad Valenciana": 0.0,
             "Extremadura": 0.0,
             "Galicia": 0.0,
-            "Madrid (Comunidad De)": 0.146720506,
+            "Madrid (Comunidad De)": 0.0,
             "Murcia (Region de)": 0.0,
             "Navarra (Comunidad Foral De)": 0.0,
             "Principado De Asturias": 0.0,
             "Rioja (La)": 0.0,
+            "Spain (Espana)": 0.0,
         }
 
     def test_weights(self):