{
  "schemaVersion": 1,
  "lessonId": "optimization-11",
  "contentHash": "885230e1c6579d0de2f25823226239c1f009704193cc86771883ac02aa1b67a7",
  "executedAt": "2026-10-20T10:45:58.425895+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "full gradient / single variance / pair variance: -2.5 1.25 0.625\nsample indices / sample gradient: [3 2] -3.5\nfull gradient / single variance / pair variance: -2.5 1.25 0.625\nsample indices / sample gradient: [3 2] -3.5\nPASS: optimization-11\n",
  "notebookCells": [
    {
      "sourceHash": "5def0fb2c0741a89030fcf064090783a9bfaa225d215c5d6458802d901bdce7b",
      "outputs": []
    },
    {
      "sourceHash": "0f0e1b023181db908648dddb4bedcb2a9e40db0a5b4a2c9f62151efa2f77bddb",
      "outputs": []
    },
    {
      "sourceHash": "f0ee8dcdc1b119f3ecdda73620d3bb1dc53cc582f73f89f68456e57e9fda813c",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "full gradient / single variance / pair variance: -2.5 1.25 0.625\n",
            "sample indices / sample gradient: [3 2] -3.5\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "99d4b450e94efb8e19d6015676a3d5f0e88eaeb895d4ff914e5d5d19bd99f096",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "full gradient / single variance / pair variance: -2.5 1.25 0.625\n",
            "sample indices / sample gradient: [3 2] -3.5\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "683761005783dfe4d525765f63651d1f67ac5021624a3795b7d53edee8bdfd78",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "f3af6d558ae9db3ebc4b7700847e5a9487bfde0255acb23ca48c392e95b15079",
      "outputs": []
    },
    {
      "sourceHash": "00808b5f090d29e7f606be6bbcc5ed915de73df277867eeab82ab0062d06b7fd",
      "outputs": []
    },
    {
      "sourceHash": "a02c471b6435aeab96a92502ca703de8d1b5dd50e6abc7655592213d96000a33",
      "outputs": []
    },
    {
      "sourceHash": "d68a5d179b16f6bbd290d59158a22a66b9b5abdaec9caaf9f40617ce22b85a8b",
      "outputs": []
    },
    {
      "sourceHash": "a7ac0f4a711008103fd7b366125846cedca04c09333ca5db28cbcf8720d0860f",
      "outputs": []
    },
    {
      "sourceHash": "4e211ba30736815b21bd9465ea758e06801270e866a007593ef753da02af5d1e",
      "outputs": []
    },
    {
      "sourceHash": "7dafb2c8d62552ca4b488fa850f4c4440e7e9379db69a6360c2d55a72a89cb16",
      "outputs": []
    },
    {
      "sourceHash": "1b813d43a9c28c518114907b7e3ef36bd3e1ac34add9fcb7931d858f2371349d",
      "outputs": []
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}