{
  "annotation_id": "63a69f02-77d7-4006-aee8-2efac4e7679a",
  "date_created": "2026-02-17T05:53:15.713000Z",
  "date_modified": "2026-02-17T05:53:15.713000Z",
  "file_hash": "e9dadd92803a3680e729cb4bd235c2e3428e839652e1210e4af558f4d76005bf",
  "private": false,
  "record": {
    "abstract": "Functions or distributions used to sample and to reconstruct signals often occur in different domains, like the Dirac delta and a band-limited bump function in classical sampling. Oblique dual frames generalize this phenomenon. In this paper, we provide new tools to study oblique dual frames and introduce a probabilistic variant of oblique dual frames. We first present the oblique dual frame potential and show that it is minimized precisely when the oblique dual coincides with the canonical oblique dual. We then define oblique dual probabilistic frames and oblique approximately dual probabilistic frames. In particular, we prove that for a given oblique dual probabilistic frame, the associated oblique dual probabilistic frame potential is minimized if and only if the frame is tight and the oblique dual is canonical. Moreover, the tightness assumption can be removed when the minimization is restricted to oblique dual probabilistic frames of pushforward type. Finally, we investigate perturbations of oblique dual probabilistic frames and show that if a probability measure is sufficiently close to an oblique dual probabilistic frame pair in the $2$-Wasserstein topology, then it forms an oblique approximately dual probabilistic frame.",
    "arxiv_id": "2601.08028",
    "authors": [
      "Dongwei Chen",
      "Emily J. King",
      "Clayton Shonkwiler"
    ],
    "categories": [
      "math.FA"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "A Land of Oblique Duality for Frames and Probabilistic Frames",
    "url": "https://arxiv.org/abs/2601.08028",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "9c546f8a-d7a3-41cc-a15e-fa3f24554bc1",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}