{
  "annotation_id": "1d28edbd-8ca8-48a6-b780-a0c7badf5e55",
  "date_created": "2026-02-17T05:53:24.066000Z",
  "date_modified": "2026-02-17T05:53:24.066000Z",
  "file_hash": "3a6902a072fa53060ff6c21230ae8cd89664c66ffc115f8d84dd89c5d558f4d4",
  "private": false,
  "record": {
    "abstract": "Given the compressed sensing measurements of an unknown vector $z \\in \\mathbb{R}^n$ using random matrices, we present a simple method to determine $z$ without solving any optimization problem or linear system. Our method uses $\\Theta(\\log n)$ random sensing matrices in $\\mathbb{R}^{k \\times n}$ and runs in $O(kn\\log n)$ time, where $k = \\Theta(s\\log n)$ and $s$ is the number of nonzero coordinates in $z$. We adapt our method to determine the support set of $z$ and experimentally compare with some optimization-based methods on binary signals.",
    "arxiv_id": "2601.10569",
    "authors": [
      "Siu-Wing Cheng",
      "Man Ting Wong"
    ],
    "categories": [
      "cs.IT",
      "math.IT"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "title": "Sparse Signal Recovery from Random Measurements",
    "url": "https://arxiv.org/abs/2601.10569",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a9bfdbc2-d4ff-4cbd-8189-2f9f9258e5a5",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}