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- Construction and compilation of the target function
- "Natively" evaluate the function. That is, without using AutoDiff. Just simple code to directly evaluate it.
- Approximate the gradient using the above native evaluation, by shifting values assigned to variables by a small epsilon.
- Use AutoDiff to evaluate the function.
- Use AutoDiff to differentiate the function.

Several tests are run for different function sizes. Given a number N, we construct a function of N variables that has N term. Each term is a linear combination of 10 variables, raised to the power of 2. We report the results for different input sizes.

- Native evaluation is supposed to be the fastest way to evaluate the function. It is used to compare how much slower is AutoDiff's evaluation compared to the native one.
- AutoDiff term construction, evaluation and differentiation are supposed to grow linearly with the input size.
- AutoDiff differentiation is supposed to be faster than gradient approximation. We claim that this is one of the main strengths of AutoDiff - you get both accuracy of exact gradient computation and speed of the linear-time computation.

N | Construct | Native eval. | Approx. Grad. | AD Eval. | AD Diff |
---|---|---|---|---|---|

1000 | 82 | 0.036 | 24.23 | 0.39 | 2.128 |

2000 | 88 | 0.079 | 104.62 | 1.132 | 6.423 |

3000 | 207 | 0.092 | 246.64 | 2.256 | 11.477 |

4000 | 342 | 0.144 | 457.78 | 3.85 | 17.075 |

5000 | 423 | 0.166 | 795.99 | 4.066 | 21.945 |

6000 | 581 | 0.2 | 1119.87 | 5.675 | 27.37 |

7000 | 568 | 0.268 | 1537.01 | 5.91 | 37.534 |

8000 | 757 | 0.265 | 2123.62 | 6.855 | 41.122 |

9000 | 902 | 0.329 | 2909.86 | 7.878 | 49.066 |

10000 | 1383 | 0.413 | 3926.53 | 8.742 | 52.101 |

- The time complexity expectations are met indeed. You can see the charts below
- On average, evaluation with AutoDiff is around 25 times slower than native evaluation. There is room for improvement!
- Differentiation using AutoDiff is orders of magnitude faster than approximating the gradient.

Last edited Apr 17, 2011 at 4:31 PM by alexshtf, version 3