{
  "annotation_id": "8e5b6886-3202-4485-a3b5-95ec6de32b78",
  "date_created": "2026-02-17T05:53:24.065000Z",
  "date_modified": "2026-02-17T05:53:24.065000Z",
  "file_hash": "fa621e80b2b65dd93f3c017fca69ae2623e1fe21a6ea8793a051fc9f14017eda",
  "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": "v2"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "8cf8e113-0184-42a0-8d22-b86f9b978b8c",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}