)]}'
{
  "commit": "3e2cdca542cab14697a0d7e411d6243f73e40d71",
  "tree": "aae60cb19bb82c75882d5f606c987d0154e1bce6",
  "parents": [
    "3ff12a878bf335dd3ea15944fe20648757c1bad8"
  ],
  "author": {
    "name": "Sameer Agarwal",
    "email": "sameeragarwal@google.com",
    "time": "Mon May 13 10:20:50 2019 -0700"
  },
  "committer": {
    "name": "Sameer Agarwal",
    "email": "sameeragarwal@google.com",
    "time": "Mon May 13 10:20:50 2019 -0700"
  },
  "message": "Make LineSearchMinizer work correctly with negative valued functions.\n\nWhen reasoning about the function_tolerance based convergence,\nLineSearchMinimizer assumed that the objective function is\npositive. This used to be the case when LineSearchMinimizer was used\nfor minimizing non-linear least squares problems. However, with\nGradientProblemSolver, the objective function can be negative (for\nexample when maximizing a function).\n\nThis change the minimizer to use the absolute value of the change\nfrom one iteration to another.\n\nhttps://github.com/ceres-solver/ceres-solver/issues/478\n\nChange-Id: I831e2db96b092374e167c582ab1480b1831d5650\n",
  "tree_diff": [
    {
      "type": "modify",
      "old_id": "ac0a19289a2badc3bfb13b4ecf3c5d6bae956414",
      "old_mode": 33188,
      "old_path": "internal/ceres/line_search_minimizer.cc",
      "new_id": "f57c047359c628f2dfadf4aba376e21675e4346b",
      "new_mode": 33188,
      "new_path": "internal/ceres/line_search_minimizer.cc"
    }
  ]
}