Many modern numerical methods in computational science and engineering rely on derivatives of mathematical models for the phenomena under investigation. The computation of these derivatives often represents the bottleneck in terms of overall runtime performance. First and higher derivative tensors need to be evaluated efficiently. The chain rule of differentiation is the fundamental prerequisite for computing accurate derivatives of composite functions which perform a potentially very large number of elemental function evaluations. Data flow dependences amongst the elemental functions give rise to a combinatorial optimization problem. We formulate {\sc Chain Rule Differentiation} and we prove it to be NP-complete. Pointers to research on its approximate solution are given.
翻译:计算学和工程的许多现代数字方法依靠数学模型的衍生物来应对所调查的现象。这些衍生物的计算往往代表整个运行时间性能的瓶颈。首先和更高衍生物加压器需要有效评估。分化链规则是计算可能进行大量元素功能评估的复合功能的准确衍生物的基本先决条件。数据流在元素函数之间的依赖导致组合优化问题。我们制定 ~ ~ 链规则差异 } 并且我们证明它是NP- 完整的。 给出了研究其近似解决方案的指针。