{
  "annotation_id": "96999174-3754-496a-a136-e5dc36d6de03",
  "date_created": "2026-02-17T05:53:15.174000Z",
  "date_modified": "2026-02-17T05:53:15.174000Z",
  "file_hash": "3b9aaeff74af4515cbdd6fea2cce32aa4b18718e7e7bfa145ce7700b5cbdea38",
  "private": false,
  "record": {
    "abstract": "This paper develops a data-driven h2 model reduction method for discrete-time linear time-invariant systems. Specifically, we solve the h2 model reduction problem defined over a finite horizon using only impulse response data. Furthermore, we show that the proposed data-driven algorithm converges to a stationary point under certain assumptions. Numerical experiments demonstrate that the proposed method constructs a good reduced-order model in terms of the h2 norm defined over the finite horizon using a SLICOT benchmark (the CD player model).",
    "arxiv_id": "2601.08372",
    "authors": [
      "Hiroki Sakamoto",
      "Kazuhiro Sato"
    ],
    "categories": [
      "eess.SY",
      "cs.SY",
      "math.OC"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Data-Driven Time-Limited h2 Optimal Model Reduction for Linear Discrete-Time Systems",
    "url": "https://arxiv.org/abs/2601.08372",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "143f3228-ac77-48d1-a580-a96bffed1239",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}