{
  "annotation_id": "9efe7c1b-5830-453c-bda2-0876d721f3d9",
  "date_created": "2026-02-17T05:53:16.194000Z",
  "date_modified": "2026-02-17T05:53:16.194000Z",
  "file_hash": "f0534188c6560b9462e883bc84553a8cdb5733d1ed097412a3d47d7da7351df7",
  "private": false,
  "record": {
    "abstract": "Recently, sharp matrix concentration inequalities~\\cite{BBvH23,BvH24} were developed using the theory of free probability. In this work, we design polynomial time deterministic algorithms to construct outcomes that satisfy the guarantees of these inequalities. As direct consequences, we obtain polynomial time deterministic algorithms for the matrix Spencer problem~\\cite{BJM23} and for constructing near-Ramanujan graphs. Our proofs show that the concepts and techniques in free probability are useful not only for mathematical analyses but also for efficient computations.",
    "arxiv_id": "2601.08111",
    "authors": [
      "Robert Wang",
      "Lap Chi Lau",
      "Hong Zhou"
    ],
    "categories": [
      "cs.DS",
      "cs.DM",
      "math.CO",
      "math.PR"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Derandomizing Matrix Concentration Inequalities from Free Probability",
    "url": "https://arxiv.org/abs/2601.08111",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a73ebaa6-fb86-4fbb-8c37-d4a8a6a244d1",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}