Now Isaac Newton and/or Joseph Raphson figured out how to do this kind of thing for all differentiable functions.

既然牛顿和拉复生已经,指数了如何解这种可导函数,因此我们就不用太担心了。

麻省理工公开课 - 计算机科学及编程导论课程节选

Successive approximation, Newton-Raphson was one nice example, but there's a whole class of things that get closer and closer, reducing your errors as you go along.

逐渐逼近,牛顿迭代是一个很好的例子,随着你不断的时行下去,你会不断的离结果越来越近，逐渐地减少误差。

麻省理工公开课 - 计算机科学及编程导论课程节选