)]}'
{
  "commit": "4f049db7c2a3ee8cf9910c6eac96be6a28a5999c",
  "tree": "ea7ee93afe4d8d51b23d1f509bbcb6ae07359d9c",
  "parents": [
    "070bba4b43b4b7449628bf456a10452fd2b34d28"
  ],
  "author": {
    "name": "Tal Ben-Nun",
    "email": "tbennun@gmail.com",
    "time": "Wed May 13 15:43:51 2015 +0300"
  },
  "committer": {
    "name": "Tal Ben-Nun",
    "email": "tbennun@gmail.com",
    "time": "Sun Aug 30 14:06:13 2015 +0300"
  },
  "message": "Adaptive numeric differentiation using Ridders\u0027 method.\n\nThis method numerically computes function derivatives in different\nscales, extrapolating between intermediate results to conserve function\nevaluations. Adaptive differentiation is essential to produce accurate\nresults for functions with noisy derivatives.\n\nFull changelist:\n-Created a new type of NumericDiffMethod (RIDDERS).\n-Implemented EvaluateRiddersJacobianColumn in NumericDiff.\n-Created unit tests with f(x) \u003d x^2 + [random noise] and\n f(x) \u003d exp(x).\n\nChange-Id: I2d6e924d7ff686650272f29a8c981351e6f72091\n",
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