nlstac：非梯度可分离非线性最小二乘拟合
nlstac: Non-Gradient Separable Nonlinear Least Squares Fitting
AI总结：
本文提出非线性最小二乘拟合软件包nlstac，实现无需初始化、仅需参数边界的非梯度可分离算法，减少非线性参数数量，并可为其他算法提供准确初始值。
摘要：
本文介绍了一个用于非线性最小二乘拟合的新软件包。该软件包实现了一种最近开发的算法，对于某些类型的非线性曲线拟合，该算法可减少需要拟合的非线性参数数量。该方法的一个显著特点是不需要初始化，而初始化通常是基于梯度的非线性拟合算法所必需的。相反，只需提供非线性参数的一些边界即可。尽管对于使用最大范数的指数衰减，该方法的收敛性已得到保证，但该算法表现出显著的鲁棒性，并且其应用已扩展到使用欧几里得范数的广泛函数。此外，该数据拟合软件包还可作为为其他依赖初始参数的算法提供准确初始参数的宝贵资源。
英文摘要：
A new package for nonlinear least squares fitting is introduced in this paper. This package implements a recently developed algorithm that, for certain types of nonlinear curve fitting, reduces the number of nonlinear parameters to be fitted. One notable feature of this method is the absence of initialization which is typically necessary for nonlinear fitting gradient-based algorithms. Instead, just some bounds for the nonlinear parameters are required. Even though convergence for this method is guaranteed for exponential decay using the max-norm, the algorithm exhibits remarkable robustness, and its use has been extended to a wide range of functions using the Euclidean norm. Furthermore, this data-fitting package can also serve as a valuable resource for providing accurate initial parameters to other algorithms that rely on them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
