diff --git a/README.md b/README.md
index 6449762..8411961 100644
--- a/README.md
+++ b/README.md
@@ -1,18 +1,18 @@
-![NAG Logo](./nag_logo.png)<a name=top></a>
+![nAG Logo](./nag_logo.png)<a name=top></a>
 
 # Content<a name=content></a>
 
-* [Examples using the NAG Library for Python](#examples)
-* [How to install the NAG Library for Python](#install)
+* [Examples using the *n*AG Library for Python](#examples)
+* [How to install the *n*AG Library for Python](#install)
 * [How to run the Jupyter notebook examples](#jupyter)
-* [List of Chapters in the NAG Library for Python](#chapters)
+* [List of Chapters in the *n*AG Library for Python](#chapters)
 * [Useful links](#links)
 
-# Examples using the NAG Library for Python <a name=examples></a>
+# Examples using the *n*AG Library for Python <a name=examples></a>
 
-This repository contains examples and demonstrations using the [NAG Library for Python](https://www.nag.com/nag-library-python).  The NAG Library for Python contains 1900+ functions spanning many areas of numerical computing and data science.
+This repository contains examples and demonstrations using the [*n*AG Library for Python](https://nag.com/nag-library/).  The *n*AG Library for Python contains 1900+ functions spanning many areas of numerical computing and data science.
 
-Designed to work alongside the open source Python packages, [Numpy](http://www.numpy.org/) and [Scipy](https://www.scipy.org/), The NAG Library for Python can augment your computational workflow in many areas.
+Designed to work alongside the open source Python packages, [Numpy](http://www.numpy.org/) and [Scipy](https://www.scipy.org/), The *n*AG Library for Python can augment your computational workflow in many areas.
 
 ## Directory of GitHub examples
 
@@ -31,26 +31,26 @@ Designed to work alongside the open source Python packages, [Numpy](http://www.n
 
 ## Examples that ship with the product
 
-In addition to those presented here, The NAG Library for Python ships with a set of usage examples.  To see them all, run the following command
+In addition to those presented here, The *n*AG Library for Python ships with a set of usage examples.  To see them all, run the following command
 
 ```
 python -m naginterfaces.library.examples --locate
 ```
 
-# How to install the NAG Library for Python<a name=install></a>
+# How to install the *n*AG Library for Python<a name=install></a>
 
-In this section we illustrate how to install the NAG Library for Python, request a Trial Licence and make sure the Library is working. Details and further information regarding the installation can be found [here](https://www.nag.com/numeric/py/nagdoc_latest/readme.html#installation).
+In this section we illustrate how to install the *n*AG Library for Python, request a Trial Licence and make sure the Library is working. Details and further information regarding the installation can be found [here](https://www.nag.com/numeric/py/nagdoc_latest/readme.html#installation).
 
 **Note** Before starting make sure you have access to a host that has Python 3 (3.4 or more recent).
 
 ### Step 1. Downloading and installing
-Installing the NAG Library is done using the `pip` package manager, fire-up a terminal and create a Python 3 virtual environment where to install and test the NAG Library
+Installing the *n*AG Library is done using the `pip` package manager, fire-up a terminal and create a Python 3 virtual environment where to install and test the *n*AG Library
 ```{bash}
 guest@nag-37:~$ python3 -m venv nag3
 guest@nag-37:~$ . nag3/bin/activate
 (nag3) guest@nag-37:~$
 ```
-Now use `pip` to install the NAG Library for Python
+Now use `pip` to install the *n*AG Library for Python
 ```{bash}
 (nag3) guest@nag-37:~$ python -m pip install --extra-index-url https://www.nag.com/downloads/py/naginterfaces_nag naginterfaces
 ```
@@ -79,7 +79,7 @@ The next step is to get the licensing info (**product code** and **KUSARI ID**)
 ```
 The output should be similar to
 ```
-The NAG Library for Python on this platform uses
+The *n*AG Library for Python on this platform uses
 underlying Library NLL6I271VL.
 This Library has been installed as part of the package
 and it requires a valid licence key.
@@ -106,7 +106,7 @@ The **two** important bits are the
  
  **Note** that the **product code** and **KUSARI ID** can be different from the previous example.
  
- With these, you are set to [contact NAG and request a trial licence](https://www.nag.com/content/software-trials?product=NAG%20Library).
+ With these, you are set to [contact *n*AG and request a trial licence](https://nag.com/contact-us/).
  
  The trial licence is a plain text chunk similar to
  ```
@@ -116,8 +116,8 @@ The **two** important bits are the
  
  The final step is to make sure the licence is valid and the library is working as expected.
  
-### Step 3. Testing the NAG Library
-The last step is to make sure the licence was correctly stored and that the NAG Library is working correctly. From the same virtual terminal re-run the Kusari licence module
+### Step 3. Testing the *n*AG Library
+The last step is to make sure the licence was correctly stored and that the *n*AG Library is working correctly. From the same virtual terminal re-run the Kusari licence module
 ```{bash}
 (nag3) guest@nag-37:~$ python -m naginterfaces.kusari
 ``` 
@@ -165,7 +165,7 @@ Run `python -m naginterfaces.library.examples --help` to see any additional usag
 # How to run the Jupyter notebook examples<a name=jupyter></a>
 
 This section briefly illustrates how to setup a host in order to open and run the [Jupyter notebooks](https://jupyter.org/) provided in this repository.
-Before running the notebooks make sure the [NAG Library is installed and working](#install). Before starting, it is advised to read [Jupyter's installation page](https://jupyter.org/install.html).
+Before running the notebooks make sure the [*n*AG Library is installed and working](#install). Before starting, it is advised to read [Jupyter's installation page](https://jupyter.org/install.html).
 
 <!-- You can [view a static render of the notebooks using Jupyter's nbviewer here](https://nbviewer.jupyter.org/github/numericalalgorithmsgroup/NAGPythonExamples/tree/master/local_optimization/) 
 [![Jupyter](https://img.shields.io/badge/launch-nbviewer-blue?logo=jupyter&logoColor=white)](https://nbviewer.jupyter.org/github/numericalalgorithmsgroup/NAGPythonExamples/tree/master/local_optimization/)
@@ -174,7 +174,7 @@ or alternatively use [Binder](https://mybinder.org/) to [view them here](https:/
 
 
 ### Installing Jupyter notebook
-To install Jupyter, launch a terminal and activate the virtual environment used to install the NAG Library for Python
+To install Jupyter, launch a terminal and activate the virtual environment used to install the *n*AG Library for Python
 ```{bash}
 guest@nag-37:~$ . nag3/bin/activate
 (nag3) guest@nag-37:~$ pip install notebook matplotlib
@@ -211,7 +211,7 @@ This command will fire-up your web browser and open the `rosenbrock2d.ipynb` not
 
 
 
-# List of Chapters in the NAG Library for Python<a name=chapters></a>
+# List of Chapters in the *n*AG Library for Python<a name=chapters></a>
 
 The following links take you to the relevant section in the official documentation
 
@@ -267,8 +267,7 @@ The following links take you to the relevant section in the official documentati
 
 # Useful links<a name=links></a>
 
-* [NAG Library for Python Documentation](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html)
-* [Request a trial licence](https://www.nag.com/content/software-trials?product=NAG%20Library)
+* [*n*AG Library for Python Documentation](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.html)
 * [Kusari licence module Documentation](https://www.nag.com/numeric/py/nagdoc_latest/naginterfaces.kusari.html#kusari)
 
 
diff --git a/local_optimization/NLDF/elastic_net.ipynb b/local_optimization/NLDF/elastic_net.ipynb
new file mode 100644
index 0000000..d52cbb3
--- /dev/null
+++ b/local_optimization/NLDF/elastic_net.ipynb
@@ -0,0 +1,536 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0edec5c6-4a7c-41d2-8241-03deed2b6acd",
+   "metadata": {},
+   "source": [
+    "# Using elastic net in a linear regression to predict a possum's length\n",
+    "\n",
+    "The routine **[handle_solve_nldf](https://support.nag.com/numeric/py/nagdoc_latest/naginterfaces.library.opt.handle_solve_nldf.html)** is a general nonlinear data-fitting solver in the [NAG® Library](https://nag.com/nag-library/) that supports a variety of different loss functions and regularization options - including elastic net.\n",
+    "\n",
+    "**Elastic net** regularization is a combination of L1 (lasso) and L2 (ridge) regularization that is ideally suited for high-dimensional and noisy data. In these cases, it can be used for **feature selection** by setting the coefficients of irrelevant features to zero. Further, it is particularly useful when dealing with **multicollinear** features - where two or more features are highly correlated. It achieves this by shrinking the coefficients of correlated features towards each other. It also helps to **reduce overfitting** by penalizing large coefficients, which can lead to better generalization performance.\n",
+    "\n",
+    "To demonstrate the use of elastic net regularization, we will build a linear regression model to predict a possum's total length based upon several features, such as, capture site, age, and head length.\n",
+    "\n",
+    "Note, the purpose of this notebook is to illustrate the use of handle_solve_nldf which is a general data-fitting framework that utilises nonlinear programming algorithms, such as sequential quadratic programming and interior point method. Therefore, it may not be as performant as one of our dedicated linear regression solvers.\n",
+    "\n",
+    "\n",
+    "**Reference:** \\\n",
+    "Lindenmayer, D. B., Viggers, K. L., Cunningham, R. B., and Donnelly, C. F. 1995. Morphological variation among columns of the mountain brushtail possum, Trichosurus caninus Ogilby (Phalangeridae: Marsupiala). Australian Journal of Zoology 43: 449-458. \\\n",
+    "Dataset source: https://www.kaggle.com/datasets/abrambeyer/openintro-possum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fcd8009c-2402-4374-a125-87f7caf0ff01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import packages\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from naginterfaces.library import opt\n",
+    "from naginterfaces.base import utils\n",
+    "\n",
+    "# Set a random seed\n",
+    "np.random.seed(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5411c4c6-2c14-40d1-86d0-c5e713f6a0ff",
+   "metadata": {},
+   "source": [
+    "## 1. Load and preprocess the data\n",
+    "This dataset has 13 features and 101 observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5497ff22-6a92-4cd5-a112-ed4a3250435b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>site</th>\n",
+       "      <th>Pop</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>hdlngth</th>\n",
+       "      <th>skullw</th>\n",
+       "      <th>totlngth</th>\n",
+       "      <th>taill</th>\n",
+       "      <th>footlgth</th>\n",
+       "      <th>earconch</th>\n",
+       "      <th>eye</th>\n",
+       "      <th>chest</th>\n",
+       "      <th>belly</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Vic</td>\n",
+       "      <td>m</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>94.1</td>\n",
+       "      <td>60.4</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>74.5</td>\n",
+       "      <td>54.5</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>36.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Vic</td>\n",
+       "      <td>f</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>92.5</td>\n",
+       "      <td>57.6</td>\n",
+       "      <td>91.5</td>\n",
+       "      <td>36.5</td>\n",
+       "      <td>72.5</td>\n",
+       "      <td>51.2</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>28.5</td>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Vic</td>\n",
+       "      <td>f</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>95.5</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>75.4</td>\n",
+       "      <td>51.9</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>34.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Vic</td>\n",
+       "      <td>f</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>93.2</td>\n",
+       "      <td>57.1</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>76.1</td>\n",
+       "      <td>52.2</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>34.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Vic</td>\n",
+       "      <td>f</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>91.5</td>\n",
+       "      <td>56.3</td>\n",
+       "      <td>85.5</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>53.2</td>\n",
+       "      <td>15.1</td>\n",
+       "      <td>28.5</td>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   site  Pop sex  age  hdlngth  skullw  totlngth  taill  footlgth  earconch  \\\n",
+       "0     1  Vic   m  8.0     94.1    60.4      89.0   36.0      74.5      54.5   \n",
+       "1     1  Vic   f  6.0     92.5    57.6      91.5   36.5      72.5      51.2   \n",
+       "2     1  Vic   f  6.0     94.0    60.0      95.5   39.0      75.4      51.9   \n",
+       "3     1  Vic   f  6.0     93.2    57.1      92.0   38.0      76.1      52.2   \n",
+       "4     1  Vic   f  2.0     91.5    56.3      85.5   36.0      71.0      53.2   \n",
+       "\n",
+       "    eye  chest  belly  \n",
+       "0  15.2   28.0   36.0  \n",
+       "1  16.0   28.5   33.0  \n",
+       "2  15.5   30.0   34.0  \n",
+       "3  15.2   28.0   34.0  \n",
+       "4  15.1   28.5   33.0  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('possum.csv', usecols=range(1,14))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "06841ec7-443e-4831-b013-3aa79cc5333a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove NAN data\n",
+    "df.dropna(axis=0,inplace=True)\n",
+    "\n",
+    "# Categorical features (site, population, sex) need to be one-hot encoded\n",
+    "df_encoded = pd.get_dummies(df, columns=['site','Pop','sex'], dtype=float, drop_first=True)\n",
+    "\n",
+    "# Extract total length (y), which is the variable to be predicted\n",
+    "y = df_encoded[[\"totlngth\"]].values\n",
+    "X = df_encoded.drop(columns=[\"totlngth\"]).values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b654fd99-5544-42d4-8467-b959b109f3cd",
+   "metadata": {},
+   "source": [
+    "## 2. Split the data into training and testing sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5c6e103d-4e2c-4640-b8d5-9f690c444192",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def train_test(X, y, test_size=0.2):\n",
+    "    \"\"\"\n",
+    "    Split dataset into training and testing sets.\n",
+    "\n",
+    "    Parameters:\n",
+    "    X (numpy array): Features\n",
+    "    y (numpy array): Observations\n",
+    "    test_size (float, optional): Proportion of data to use for testing\n",
+    "\n",
+    "    Returns:\n",
+    "    X_train, y_train, X_test, y_test\n",
+    "    \"\"\"\n",
+    "    # Get total number of samples\n",
+    "    num_samples = X.shape[0]\n",
+    "\n",
+    "    # Calculate number of test samples\n",
+    "    num_test_samples = int(num_samples * test_size)\n",
+    "\n",
+    "    # Generate random indices for training set\n",
+    "    train_indices = np.random.choice(num_samples, num_samples - num_test_samples, replace=False)\n",
+    "\n",
+    "    # Create training sets\n",
+    "    X_train = X[train_indices]\n",
+    "    y_train = y[train_indices]\n",
+    "\n",
+    "    # Create testing sets\n",
+    "    test_indices = np.setdiff1d(np.arange(num_samples), train_indices)\n",
+    "    X_test = X[test_indices]\n",
+    "    y_test = y[test_indices]\n",
+    "\n",
+    "    return X_train, y_train, X_test, y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "86657422-9e33-45ff-899f-7f7840150914",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def scale_data(X_train, y_train, X_test, y_test):\n",
+    "    \"\"\"\n",
+    "    Scale the training and testing datasets.\n",
+    "\n",
+    "    Returns:\n",
+    "    X_train, y_train, X_test, y_test\n",
+    "    \"\"\"\n",
+    "    mu = X_train.mean(0)\n",
+    "    sigma = X_train.std(0)\n",
+    "    for j in range(X_train.shape[-1]):\n",
+    "        xs = X_train[:,j]\n",
+    "        is_categorical = np.logical_or(np.isclose(xs, 1.), np.isclose(xs, 0.)).all()\n",
+    "        if not is_categorical:\n",
+    "            X_train[:,j] = (X_train[:,j] - mu[j]) / sigma[j]\n",
+    "            X_test[:,j] = (X_test[:,j] - mu[j]) / sigma[j]\n",
+    "    \n",
+    "    y_test = (y_test - y_train.mean()) / y_train.std()\n",
+    "    y_train = (y_train - y_train.mean()) / y_train.std()\n",
+    "\n",
+    "    return X_train, y_train, X_test, y_test\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "487c2e0f-f494-4ac3-8c34-1db320178693",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set shapes: (81, 17) (81, 1)\n",
+      "Testing set shapes: (20, 17) (20, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Split data into training and testing sets\n",
+    "X_train, y_train, X_test, y_test = train_test(X, y)\n",
+    "\n",
+    "# Scale the data\n",
+    "X_train, y_train, X_test, y_test = scale_data(X_train, y_train, X_test, y_test)\n",
+    "\n",
+    "print(\"Training set shapes:\", X_train.shape, y_train.shape)\n",
+    "print(\"Testing set shapes:\", X_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7acd46ae-881f-4d5d-b174-0ee5a66703b7",
+   "metadata": {},
+   "source": [
+    "## 3. Fit a linear regression with least squares loss and elastic net regularization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ad4796a5-724c-4a55-b485-4b18ce603328",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " E04GN, Nonlinear Data-Fitting\n",
+      " Status: converged, an optimal solution found\n",
+      " Final objective value  1.652821E+01\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Number of variables = number of features + bias term\n",
+    "nvar = X_train.shape[1] + 1\n",
+    "\n",
+    "# Create a handle for the model\n",
+    "handle = opt.handle_init(nvar=nvar)\n",
+    "\n",
+    "# Register residual structure\n",
+    "nres =  X_train.shape[0]\n",
+    "opt.handle_set_nlnls(handle, nres)\n",
+    "\n",
+    "# Create the data structure to be passed to the solver\n",
+    "data = {}\n",
+    "data[\"X_train\"] = X_train\n",
+    "data[\"y_train\"] = y_train\n",
+    "\n",
+    "# Define the residual callback function and its gradient\n",
+    "def lsqfun(x, nres, inform, data):\n",
+    "    rx = np.zeros(nres, dtype=float)\n",
+    "    X_train = data[\"X_train\"]\n",
+    "    y_train = data[\"y_train\"].squeeze()\n",
+    "    \n",
+    "    # Fit a linear regression to the data\n",
+    "    r_full = y_train - (x[0] + X_train @ x[1:]) \n",
+    "    for i in range(nres):\n",
+    "        rx[i] = r_full[i]\n",
+    " \n",
+    "    return rx, inform\n",
+    "    \n",
+    "def lsqgrd(x, nres, rdx, inform, data):\n",
+    "    X_train = data[\"X_train\"]\n",
+    "\n",
+    "    for i in range(nres):\n",
+    "        for j in range(nvar):\n",
+    "            if j==0:\n",
+    "                rdx[i*nvar] = -1               \n",
+    "            else:\n",
+    "                rdx[i*nvar + j] = -X_train[i, j-1]\n",
+    "            \n",
+    "    return inform\n",
+    "\n",
+    "# Set loss function to l2-norm, elastic net regularization, and printing options\n",
+    "for option in [\n",
+    "    'NLDF Loss Function Type = L2',\n",
+    "    'Print Level = 1',\n",
+    "    'Print Options = No',\n",
+    "    'Reg Term Type = Elastic Net',\n",
+    "    ]:\n",
+    "    opt.handle_opt_set (handle, option)\n",
+    "\n",
+    "# Use an explicit I/O manager for abbreviated iteration output\n",
+    "iom = utils.FileObjManager(locus_in_output=False)\n",
+    "\n",
+    "# Set initial guess and solve\n",
+    "x = np.array([np.random.rand() for _ in range(nvar)])\n",
+    "\n",
+    "sol_en = opt.handle_solve_nldf(handle, lsqfun, lsqgrd, x, nres, data=data, io_manager=iom)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b84636fe-1a3c-4188-8dec-7449dbc059b2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " E04GN, Nonlinear Data-Fitting\n",
+      " Status: converged, an optimal solution found\n",
+      " Final objective value  1.362383E+01\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Resolve the problem with no regularization\n",
+    "opt.handle_opt_set(handle, 'Reg Term Type = Off')\n",
+    "sol_noreg = opt.handle_solve_nldf(handle, lsqfun, lsqgrd, x, nres, data=data, io_manager=iom)\n",
+    "\n",
+    "# Destroy the handle\n",
+    "opt.handle_free(handle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ada90e01-6845-41b8-9f91-9944497a94ed",
+   "metadata": {},
+   "source": [
+    "## 4. Compute root mean square error (RMSE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "126cca3a-2b1e-461b-af09-df70c8c636b8",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def calculate_rmse(y_actual, y_pred):\n",
+    "    \"\"\"\n",
+    "    Calculate the Root Mean Square Error (RMSE) between two lists of numbers\n",
+    "\n",
+    "    Args:\n",
+    "        y_actual (list): The actual values\n",
+    "        y_pred (list): The predicted values\n",
+    "\n",
+    "    Returns:\n",
+    "        float: The Root Mean Squared Error\n",
+    "    \"\"\"\n",
+    "    return np.sqrt(np.square(y_actual - y_pred).mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "207f4ff1-b1c3-4c54-81ff-4789fa3bbad0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using elastic net regularization decreased the RMSE by 0.034 compared to using no regularization.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate predicted values for y with elastic net regularization and find RMSE\n",
+    "y_pred_en = sol_en.x[0] + X_test @ sol_en.x[1:]\n",
+    "rmse_elastic_net = calculate_rmse(y_test, y_pred_en)\n",
+    "\n",
+    "# Calculate predicted values for y with no regularization and find RMSE\n",
+    "y_pred_noreg = sol_noreg.x[0] + X_test @ sol_noreg.x[1:]\n",
+    "rmse_noreg = calculate_rmse(y_test, y_pred_noreg)\n",
+    "\n",
+    "# Report the difference in RMSE\n",
+    "print(f\"Using elastic net regularization decreased the RMSE by {round(rmse_noreg - rmse_elastic_net, 4)} compared to using no regularization.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4dfdf65a-c992-4c48-ba6a-0a8438328218",
+   "metadata": {},
+   "source": [
+    "For more information on the NAG® Library and our [Optimization Modelling Suite](https://nag.com/mathematical-optimization/) or to try it for yourself, visit [‘Getting Started with the NAG Library’](https://support.nag.com/content/getting-started-nag-library?_gl=1*xmlppm*_gcl_au*MTEwNDczODM2NS4xNzIyMDAyNzkz*_ga*MjA2NzgxMjY0NS4xNzIyMDAyNzk0*_ga_6MCQDQP46G*MTcyMzEzNDUxNi41LjAuMTcyMzEzNDUzNy4zOS4wLjA.), select your configuration and language, download the software, request a trial key and experiment for yourself."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/local_optimization/NLDF/possum.csv b/local_optimization/NLDF/possum.csv
new file mode 100644
index 0000000..0f7cc0f
--- /dev/null
+++ b/local_optimization/NLDF/possum.csv
@@ -0,0 +1,105 @@
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