# DynamicAutoDiffCostFunction

```cpp
class DynamicAutoDiffCostFunction
```

`AutoDiffCostFunction` 在编译的时候需要确定每个参数块的大小，在一些应用场景中，这往往是不现实的，例如 Bezier curve fitting, Neural Network training 等。

```cpp
template <typename CostFunctor, int Stride = 4>
class DynamicAutoDiffCostFunction : public CostFunction {
};
```

在这些场景下，可以选择使用 `DynamicAutoDiffCostFunction` ，和 `AutoDiffCostFunction` 一样，用户必须定义一个模板函数，但是语法上略有不同，语法大概如下

```cpp
struct MyCostFunctor {
  template<typename T>
  bool operator()(T const* const* parameters, T* residuals) const {
  }
}
```

因为参数块的大小是在运行时确定的，因此用户在创建完 `DynamicAutoDiffCostFunction` 之后必须指定大小，例如

```cpp
auto* cost_function = new DynamicAutoDiffCostFunction<MyCostFunctor, 4>();
cost_function->AddParameterBlock(5);
cost_function->AddParameterBlock(10);
cost_function->SetNumResiduals(21);
```

Under the hood, the implementation evaluates the cost function multiple times, computing a small set of the derivatives (four by default, controlled by the `Stride` template parameter) with each pass. There is a performance tradeoff with the size of the passes; Smaller sizes are more cache efficient but result in larger number of passes, and larger stride lengths can destroy cache-locality while reducing the number of passes over the cost function. The optimal value depends on the number and sizes of the various parameter blocks. 根据经验，在使用 `DynamicAutoDiffCostFunction` 之前，请尝试使用 `AutoDiffCostFunction`。
