)]}'
{
  "commit": "b22d063075ec545a59a25abd5d83e4642dc329c2",
  "tree": "0529893c06e6ef5837468563314d80340053c8cc",
  "parents": [
    "f258e4624f5bd86105ea28b9b92dd70a3f4a3a44"
  ],
  "author": {
    "name": "Sameer Agarwal",
    "email": "sameeragarwal@google.com",
    "time": "Thu Aug 15 22:55:23 2013 -0700"
  },
  "committer": {
    "name": "Sameer Agarwal",
    "email": "sameeragarwal@google.com",
    "time": "Fri Aug 16 10:48:54 2013 -0700"
  },
  "message": "Reduce memory usage in covariance estimation.\n\nWhen using the SPARSE_QR algorithm, now a Q-less\nfactorization is used. This results in significantly\nless memory usage.\n\nThe inversion of the semi-normal equations is now\nthreaded using openmp. Indeed if one has SuiteSparse\ncompiled with TBB, then both the factorization\nand the inversion are completely threaded.\n\nChange-Id: Ia07591e48e7958d427ef91ff9e67662f6e982c21\n",
  "tree_diff": [
    {
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      "old_mode": 33188,
      "old_path": "internal/ceres/compressed_col_sparse_matrix_utils.h",
      "new_id": "494a8a5c8ae78cd5b4d6c47f6063cf52ecd7cdba",
      "new_mode": 33188,
      "new_path": "internal/ceres/compressed_col_sparse_matrix_utils.h"
    },
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      "new_id": "3faf06ca5262dc03e4feb7dbc520df5af8801bb4",
      "new_mode": 33188,
      "new_path": "internal/ceres/compressed_col_sparse_matrix_utils_test.cc"
    },
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      "new_path": "internal/ceres/covariance_impl.cc"
    },
    {
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}