)]}'
{
  "commit": "5742b7d0f14d2d170054623ccfee09ea214b8ed9",
  "tree": "fefc26c0d0a057494f331bb23e06f8c54eb665be",
  "parents": [
    "d0b6cf657d6ef0dd739e958af9a5768f2eecfd35"
  ],
  "author": {
    "name": "Sameer Agarwal",
    "email": "sameeragarwal@google.com",
    "time": "Wed Aug 26 09:24:33 2015 -0700"
  },
  "committer": {
    "name": "Sameer Agarwal",
    "email": "sameeragarwal@google.com",
    "time": "Sat Sep 05 14:31:52 2015 -0700"
  },
  "message": "Improve performance of SPARSE_NORMAL_CHOLESKY + dynamic_sparsity\n\nThe outer product computation logic in SparseNormalCholeskySolver\ndoes not work well with dynamic sparsity. The overhead of computing\nthe sparsity pattern of the normal equations is only amortized if\nthe sparsity is constant. If the sparsity can change from call to call\nSparseNormalCholeskySolver will actually be more expensive.\n\nFor Eigen and for CXSparse we now explicitly compute the normal\nequations using their respective matrix-matrix product routines and solve.\nChange-Id: Ifbd8ed78987cdf71640e66ed69500442526a23d4\n",
  "tree_diff": [
    {
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    },
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      "old_mode": 33188,
      "old_path": "internal/ceres/sparse_normal_cholesky_solver.cc",
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      "new_path": "internal/ceres/sparse_normal_cholesky_solver.cc"
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}