非線性最小平方法之最適曲線

Nonlinear Least Squares Curre Fitting

由一群资料寻找最适曲线是科学分支中一重要的工作。于本文中，我们研究最小平方法(Least squares algorithm)，辛普勒斯法(Simplex algorithm)及马克特法(Marquardt's algorithm)并于IBM PC上实际比较其效率、精确度及其方法之适用性。

Fitting curves to data is an important task in various branches of science. In this paper， we investigate and implement (on an IBM-PC) the standard linear least squares algorithm， the simplex algorithm and Marquardt's algorithm. In particular， we shall compare the efficiency. accuracy， and general applicability of these algorithms.