Initial commit

README.md

+# Matrix Analysis, spring semester 2025
+
+This repository contains the notebooks for the exercises sessions of the EPFL bachelor course [EE-312 Matrix Analysis](https://edu.epfl.ch/studyplan/en/bachelor/electrical-and-electronics-engineering/coursebook/matrix-analysis-EE-312) ([moodle](https://moodle.epfl.ch/course/view.php?id=16942)).
+
+The course will address the following topics:
+- Linear transformations
+- Pseudo-inverse
+- Projections and norms
+- Eigenvalues, eigenvectors and eigenvalue problems
+- Singular Value Decomposition
+- Linear Equations
+- Random matrices
+- Linear Least Squares
+- Linear differential and difference equations
+
+
+## Installation
+
+### Noto
+You can run the notebooks using EPFL's jupyterhub service <https://noto.epfl.ch>. In this case you only need to login into noto, clone this repository using the `Git -> Clone a repository` menu. Noto's default environment already has all the required packages pre-installed.
+
+### Local installation
+For a local installation, you will need [git], [Python], and packages such as [numpy](https://numpy.org) or [Python scientific stack][scipy].
+If you don't know how to install those on your platform, we recommend to install [Miniconda], a distribution of the [conda] package and environment manager.
+Follow the below instructions to install it and create an environment for the course.
+
+1. Download the Python 3.x installer for Windows, macOS, or Linux from <https://conda.io/miniconda.html> and install with default settings. For windows, it is advised to use the the Windows Subsystem for Linux (WSL) which allows you to run all Linux commands and applications within Windows. In order to use it, you need to install it first, e.g. for [Ubuntu](https://ubuntu.com/wsl) (but other Linux distributions are available as well). Once installed, open the Ubuntu WSL and proceed with Miniconda installation for Linux and create the environment.
+   Skip this step if you have conda already installed.
+   * macOS: double-click on `Miniconda3-latest-MacOSX-x86_64.pkg` or run `bash Miniconda3-latest-MacOSX-x86_64.sh` in a terminal.
+   * Linux: run `bash Miniconda3-latest-Linux-x86_64.sh` in a terminal or use your package manager.
+1. Open a terminal.
+   Windows: open the Anaconda Prompt from the Start menu.
+1. Install git with `conda install git`.
+1. Navigate to the folder where you want to store the course material with `cd path/to/folder`.
+1. Download this repository with `git clone https://gitlab.epfl.ch/lts2/matrix-analysis-2024`.
+1. Enter the repository with `cd matrix-analysis-2025`.
+1. Create an environment with the packages required for the course with `conda env create -f environment.yml`.
+1. Run the steps below to start Jupyter. You should be able to run the [`test_install.ipynb`][test_install] notebook.
+
+Every time you want to work, do the following:
+
+1. Open a terminal. Windows: open the Anaconda Prompt from the Start menu.
+1. Activate the environment with `conda activate matrix-analysis-2025`.
+1. Navigate to the folder where you stored the course material with `cd path/to/folder/matrix-analysis-2025`.
+1. Start Jupyter with `jupyter lab`. The command should open a new tab in your web browser.
+1. Edit and run the notebooks from your browser.
+1. Once done, you can run `conda deactivate` to leave the `matrix-analysis-2024` environment.
+
+
+[git]: https://git-scm.com
+[python]: https://www.python.org
+[scipy]: https://www.scipy.org
+[anaconda]: https://www.anaconda.com/download
+[miniconda]: https://conda.io/miniconda.html
+[conda]: https://conda.io
+[conda-forge]: https://conda-forge.org
+[test_install]: https://nbviewer.org/github/epfl-lts2/matrix-analysis-2025/blob/master/test_install.ipynb
+
+### Binder/Colab...
+You can also run those notebooks using other online services such as [binder](https://mybinder.org) or [Google colab](https://colab.research.google.com/).
+
+Clicking on one of the badges below will open this repository in binder/colab.
+
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/epfl-lts2/matrix-analysis-2024/HEAD)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/epfl-lts2/matrix-analysis-2024/)
+
+### Workaround for MacOS swiss layout keyboards
+Jupyterlab suffers from an annoying quirk when using a swiss layout keyboard on MacOs which prevents you from (simply) typing the '@' character in a cell using `Alt + G`. This is a [known issue](https://github.com/jupyterlab/jupyterlab/issues/7704), for which a workaround exists.
+
+Create a keyboard shortcut: open `Settings>Advanced Settings>Keyboard Shortcuts` and add the following configuration block:
+```
+{
+  shortcuts: [
+    {
+      command: 'apputils:run-first-enabled',
+      selector: 'body',
+      keys: [
+        'Alt G',
+      ],
+      args: {
+        commands: [
+          'console:replace-selection',
+          'fileeditor:replace-selection',
+          'notebook:replace-selection',
+        ],
+        args: {
+          text: '@',
+        },
+      },
+    },
+  ],
+}
+``` 
+
+## Team
+
+* Lecturer:
+[Pierre Vandergheynst](https://people.epfl.ch/pierre.vandergheynst)
+* Assistants:
+[Nicolas Aspert](https://people.epfl.ch/nicolas.aspert),

environment.yml

+name: matrix-analysis-2025
+channels:
+  - defaults
+dependencies:
+  - jupyterlab=4.2.5
+  - matplotlib=3.9.2
+  - numpy=1.26.4
+  - pandas=2.2.3
+  - python=3.11.10
+  - requests=2.32.3
+  - scikit-image=0.24.0
+  - scikit-learn=1.5.1
+  - scipy=1.14.1
+  
\ No newline at end of file

test_install.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0e44bd91-51a7-4c56-92e5-c196be7941af",
+   "metadata": {},
+   "source": [
+    "# [Matrix Analysis'24]: test your installation\n",
+    "[matrix analysis'24]: https://github.com/epfl-lts2/matrix-analysis-2024\n",
+    "\n",
+    "[EPFL LTS2](https://lts2.epfl.ch)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81936a03-dd2d-4348-af93-828a107ec5c1",
+   "metadata": {},
+   "source": [
+    "This is a mini \"test\" Jupyter notebook to make sure the main packages we'll use are installed.\n",
+    "Run it after following the [installation instructions](https://github.com/epfl-lts2/matrix-analysis-2024#installation)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2dc4d30c-4162-4347-9ea1-7bc5249c81ee",
+   "metadata": {},
+   "source": [
+    "## Standalone dependencies\n",
+    "\n",
+    "If you get a `command not found` error, try to run `conda install <package-name>` (in the `matrix-analysis-2024` environment, i.e., after `conda activate matrix-analysis-2024`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4737f499-386c-463b-8630-85e7bfac5acc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!conda --version # this will fail on Noto "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec0596c7-967b-4399-81da-6602bbdcc17d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git --version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0862c7bf-8ae0-4f9a-b96a-1698e59e4aed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from platform import python_version\n",
+    "\n",
+    "print(python_version())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41138a01-2426-4cbf-9661-a7e780f06ef4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!jupyter --version\n",
+    "\n",
+    "!ipython --version\n",
+    "\n",
+    "!jupyter-lab --version\n",
+    "!jupyter-notebook --version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3d1d1b1-f09f-44ad-ba14-726451d9898b",
+   "metadata": {},
+   "source": [
+    "## Python packages\n",
+    "\n",
+    "If you get a `ModuleNotFoundError` error, try to run `conda install <package-name>` (in the `matrix-analysis-2023` environment, i.e., after `conda activate matrix-analysis-2024`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1141b09-2122-4862-8b69-2c9d40187662",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "np.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9537241-0f85-4d72-a62f-039826aa7f8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy\n",
+    "scipy.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3860668d-bb4c-4121-94d5-f4655167afc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "mpl.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08ef3716-82c6-4d00-bdb8-e9d3225c8b69",
+   "metadata": {},
+   "source": [
+    "## Small test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0aa07bd9-448b-4ceb-a3d8-15109cb439e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "734b2128-a529-491b-b75e-71874db8de71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_vect(x, b, xlim, ylim):\n",
+    "    '''\n",
+    "    function to plot two vectors, \n",
+    "    x - the original vector\n",
+    "    b - the transformed vector\n",
+    "    xlim - the limit for x\n",
+    "    ylim - the limit for y\n",
+    "    '''\n",
+    "    plt.figure(figsize = (10, 6))\n",
+    "    plt.quiver(0,0,x[0],x[1],\\\n",
+    "        color='k',angles='xy',\\\n",
+    "        scale_units='xy',scale=1,\\\n",
+    "        label='Original vector')\n",
+    "    plt.quiver(0,0,b[0],b[1],\\\n",
+    "        color='g',angles='xy',\\\n",
+    "        scale_units='xy',scale=1,\\\n",
+    "        label ='Transformed vector')\n",
+    "    plt.xlim(xlim)\n",
+    "    plt.ylim(ylim)\n",
+    "    plt.xlabel('X')\n",
+    "    plt.ylabel('Y')\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c493443b-930d-49ba-ae19-823e5dae362c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.array([[2, 0],[0, 1]])\n",
+    "\n",
+    "x = np.array([1, 1])\n",
+    "b = np.dot(A, x.T)\n",
+    "plot_vect(x,b,(0,3),(0,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6002c2a-1d9c-4ca3-aa11-7f4b5cf0c30a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}