{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bc616b35-0eec-43ae-8a7a-bb87891879a0",
   "metadata": {},
   "source": [
    "# Compressed sensing example\n",
    "[Notebook file](https://www.inp.nsk.su/~petrenko/notebooks/compressed_sensing.ipynb)\n",
    "\n",
    "[Qwen](https://chat.qwen.ai/s/05f97601-7fc8-4436-adff-9d29b2453c03?fev=0.2.63), [A. Petrenko](https://www.inp.nsk.su/~petrenko/) (2026) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c19e21fa-47b1-4c30-a396-e08b358ab98e",
   "metadata": {},
   "source": [
    "# Theory: How and Why Compressed Sensing Works\n",
    "\n",
    "### 1. The Problem: Underdetermined Systems\n",
    "We are trying to solve a linear system $y = Ax$, where:\n",
    "- $A$ is an $m \\times n$ measurement matrix.\n",
    "- $y$ is the vector of $m$ measurements (observations).\n",
    "- $x$ is the vector of $n$ unknowns (the signal we want to recover).\n",
    "\n",
    "In our example, $m = 50$ and $n = 500$. Because $m \\ll n$, the system is heavily **underdetermined**. Geometrically, the equation $Ax = y$ defines a high-dimensional hyperplane, meaning there are *infinitely many* solutions for $x$ that perfectly satisfy the measurements.\n",
    "\n",
    "### 2. The Sparsity Prior\n",
    "To find the *correct* solution among infinitely many, we need a prior assumption. In **Compressed Sensing**, we assume the true signal $x$ is **$k$-sparse**. This means that out of $n = 500$ unknowns, only $k = 5$ are non-zero. We don't know *which* 5 indices are non-zero, but we know the count is very small.\n",
    "\n",
    "### 3. Why $L_2$ Minimization (Least Squares) Fails\n",
    "The standard approach to underdetermined systems is to find the solution with the minimum $L_2$ norm: \n",
    "$$ \\min \\|x\\|_2 \\quad \\text{subject to} \\quad Ax = y $$\n",
    "Geometrically, the $L_2$ \"unit ball\" is a smooth, round sphere. When the affine subspace $Ax = y$ expands and touches this sphere, the point of contact will almost never lie exactly on a coordinate axis. As a result, the $L_2$ solution spreads the \"energy\" evenly across all 500 variables, resulting in a dense, noisy-looking vector that completely fails to recover the original 5 spikes.\n",
    "\n",
    "### 4. Why $L_1$ Minimization (Compressed Sensing) Succeeds\n",
    "Compressed sensing replaces the $L_2$ norm with the **$L_1$ norm**:\n",
    "$$ \\min \\|x\\|_1 \\quad \\text{subject to} \\quad Ax = y $$\n",
    "Geometrically, the $L_1$ \"unit ball\" in high dimensions is a cross-polytope (like a diamond in 2D or an octahedron in 3D). Crucially, this shape has **sharp corners (spikes) that lie exactly on the coordinate axes**. \n",
    "\n",
    "When the affine subspace $Ax = y$ expands and intersects this $L_1$ ball, it is highly probable that the *first* point of contact will be one of these sharp corners. A corner corresponds to a solution where most coordinates are **exactly zero**. This geometric property is what allows $L_1$ minimization to naturally promote sparsity and recover the true signal.\n",
    "\n",
    "### 5. The Mathematical Guarantee: Restricted Isometry Property (RIP)\n",
    "For $L_1$ minimization to work reliably, the measurement matrix $A$ must not \"hide\" sparse signals. This is formalized by the **Restricted Isometry Property (RIP)**. A matrix satisfies the RIP if it acts almost like an orthogonal matrix when restricted to sparse vectors (i.e., it preserves the lengths of sparse vectors). \n",
    "\n",
    "Random matrices with independent Gaussian entries (like `np.random.randn` in our code) satisfy the RIP with overwhelming probability. Theoretical guarantees state that exact recovery is possible provided the number of measurements $m$ satisfies:\n",
    "$$ m \\ge \\mathcal{O}\\left(k \\log\\left(\\frac{n}{k}\\right)\\right) $$\n",
    "For $k=5$ and $n=500$, the required $m$ is roughly $5 \\log(100) \\approx 23$. Our choice of $m=50$ is more than enough to guarantee perfect recovery!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a6992ace-b4bf-4e0d-979d-434d249ae489",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System dimensions: 50 equations, 500 unknowns, 5 non-zeros\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import linprog\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "np.random.seed(17)\n",
    "\n",
    "# Problem dimensions\n",
    "n = 500  # Number of unknowns (signal length)\n",
    "m = 50   # Number of equations (measurements)\n",
    "k = 5    # Number of non-zero elements (sparsity)\n",
    "\n",
    "print(f\"System dimensions: {m} equations, {n} unknowns, {k} non-zeros\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "efa6ea1b-7824-4f6c-a052-6afb6868d97a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True non-zero indices: [336 178  43 135 316]\n",
      "True non-zero values:  [-5.67326905 -2.72795721  8.78510681 -8.09743749 -6.63558559]\n"
     ]
    }
   ],
   "source": [
    "# 1. Generate a random Gaussian measurement matrix A\n",
    "# Gaussian matrices satisfy the Restricted Isometry Property (RIP) with high probability\n",
    "A = np.random.randn(m, n)\n",
    "\n",
    "# 2. Generate the true k-sparse signal x_true\n",
    "x_true = np.zeros(n)\n",
    "# Randomly choose k indices to be non-zero\n",
    "nonzero_indices = np.random.choice(n, k, replace=False)\n",
    "# Assign random values to these indices\n",
    "x_true[nonzero_indices] = np.random.randn(k) * 10  \n",
    "\n",
    "# 3. Generate the measurements y\n",
    "y = A @ x_true\n",
    "\n",
    "print(f\"True non-zero indices: {nonzero_indices}\")\n",
    "print(f\"True non-zero values:  {x_true[nonzero_indices]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "169646d7-2b92-482c-a52d-46e12378a5ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L1 Minimization (Compressed Sensing) succeeded!\n"
     ]
    }
   ],
   "source": [
    "# To solve min ||x||_1 subject to Ax = y using linear programming,\n",
    "# we split x into positive and negative parts: x = u - v, where u >= 0, v >= 0.\n",
    "# Then ||x||_1 = sum(u) + sum(v).\n",
    "\n",
    "# Objective function: minimize sum(u) + sum(v)\n",
    "c = np.ones(2 * n)\n",
    "\n",
    "# Equality constraint: A(u - v) = y  =>  [A, -A] @ [u; v] = y\n",
    "A_eq = np.hstack([A, -A])\n",
    "b_eq = y\n",
    "\n",
    "# Bounds: u >= 0, v >= 0\n",
    "bounds = [(0, None) for _ in range(2 * n)]\n",
    "\n",
    "# Solve the linear program using HiGHS (modern, robust SciPy solver)\n",
    "res = linprog(c, A_eq=A_eq, b_eq=b_eq, bounds=bounds, method='highs')\n",
    "\n",
    "if res.success:\n",
    "    # Reconstruct x from u and v\n",
    "    u_v = res.x\n",
    "    x_l1 = u_v[:n] - u_v[n:]\n",
    "    print(\"L1 Minimization (Compressed Sensing) succeeded!\")\n",
    "else:\n",
    "    print(\"L1 Minimization failed:\", res.message)\n",
    "    x_l1 = np.zeros(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0fb8648d-a541-4be5-90e0-f633a70b8d52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L2 Minimization (Least Squares) completed.\n"
     ]
    }
   ],
   "source": [
    "# L2 minimization (Minimum energy solution) using Moore-Penrose pseudo-inverse\n",
    "x_l2 = np.linalg.pinv(A) @ y\n",
    "print(\"L2 Minimization (Least Squares) completed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6ccf3f59-4c5f-4f85-9e2a-bb97ccca855f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/data/shared/examples/Compressed_Sensing'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d35aa03c-8b07-459c-a0c1-d0b296bd3d29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "L2 Reconstruction Error: 1.4367e+01\n",
      "L1 Reconstruction Error: 1.0436e-13\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate reconstruction errors\n",
    "error_l1 = np.linalg.norm(x_true - x_l1, 2)\n",
    "error_l2 = np.linalg.norm(x_true - x_l2, 2)\n",
    "\n",
    "print(f\"\\nL2 Reconstruction Error: {error_l2:.4e}\")\n",
    "print(f\"L1 Reconstruction Error: {error_l1:.4e}\")\n",
    "\n",
    "# Plotting\n",
    "fig, axes = plt.subplots(3, 1, figsize=(10, 10))\n",
    "\n",
    "# Plot 1: True Signal\n",
    "axes[0].stem(x_true, markerfmt='C0o', linefmt='C0-', basefmt=\" \")\n",
    "axes[0].set_title(f\"True Signal (k={k} non-zeros)\")\n",
    "axes[0].set_xlabel(\"Index\")\n",
    "axes[0].set_ylabel(\"Amplitude\")\n",
    "axes[0].set_xlim(0, n)\n",
    "\n",
    "# Plot 2: L1 Reconstruction (Compressed Sensing)\n",
    "axes[1].stem(x_l1, markerfmt='C1o', linefmt='C1-', basefmt=\" \")\n",
    "axes[1].set_title(f\"L1 Minimization (Error: {error_l1:.2e})\")\n",
    "axes[1].set_xlabel(\"Index\")\n",
    "axes[1].set_ylabel(\"Amplitude\")\n",
    "axes[1].set_xlim(0, n)\n",
    "\n",
    "# Plot 3: L2 Reconstruction (Least Squares)\n",
    "axes[2].stem(x_l2, markerfmt='C2o', linefmt='C2-', basefmt=\" \")\n",
    "axes[2].set_title(f\"L2 Minimization (Error: {error_l2:.2e})\")\n",
    "axes[2].set_xlabel(\"Index\")\n",
    "axes[2].set_ylabel(\"Amplitude\")\n",
    "axes[2].set_xlim(0, n)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "95bb78b7-6bb5-417d-8c68-58cdea93ba7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/data/shared/examples/Compressed_Sensing'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1c687955-d8aa-47cd-9085-af3d94b958c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "19a0847a-4652-4a68-ba8c-a75ae954deb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.12.2\n",
      "IPython version      : 8.25.0\n",
      "\n",
      "Compiler    : GCC 12.3.0\n",
      "OS          : Linux\n",
      "Release     : 6.17.0-35-generic\n",
      "Machine     : x86_64\n",
      "Processor   : x86_64\n",
      "CPU cores   : 32\n",
      "Architecture: 64bit\n",
      "\n",
      "numpy     : 1.26.4\n",
      "matplotlib: 3.8.4\n",
      "sys       : 3.12.2 | packaged by conda-forge | (main, Feb 16 2024, 20:50:58) [GCC 12.3.0]\n",
      "json      : 2.0.9\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark --python --date --iversions --machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dc12e34d-9df3-4090-8e4f-73ee6277ebbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook compressed_sensing.ipynb to HTML\n",
      "[NbConvertApp] WARNING | Alternative text is missing on 1 image(s).\n",
      "[NbConvertApp] Writing 399405 bytes to compressed_sensing.html\n"
     ]
    }
   ],
   "source": [
    "!jupyter nbconvert --to HTML compressed_sensing.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ed3949a-a06e-41f3-aa70-6fcb4c8196cc",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}