论文标题
JAXFIT：非线性最小二乘曲线的信任区域方法在GPU上拟合
JAXFit: Trust Region Method for Nonlinear Least-Squares Curve Fitting on the GPU
论文作者
论文摘要
我们在GPU上实现了一种信任区域方法，用于使用称为JAX的新的深度学习Python库，用于非线性最小二乘曲线曲线拟合问题。我们的开源软件包JaxFit可用于无约束和约束曲线拟合问题，并允许仅在Python中定义拟合功能 - 而无需对GPU或CUDA编程的任何专业知识。由于JaxFit在GPU上运行，尽管非常易于使用，但它比基于CPU的库甚至其他基于GPU的库快得多。此外，由于JAX的深度学习基础，JaxFit的信任区域算法中的Jacobian是通过自动差异来计算的，而不是使用衍生近似值或要求用户定义拟合函数的部分导数。
We implement a trust region method on the GPU for nonlinear least squares curve fitting problems using a new deep learning Python library called JAX. Our open source package, JAXFit, works for both unconstrained and constrained curve fitting problems and allows the fit functions to be defined in Python alone -- without any specialized knowledge of either the GPU or CUDA programming. Since JAXFit runs on the GPU, it is much faster than CPU based libraries and even other GPU based libraries, despite being very easy to use. Additionally, due to JAX's deep learning foundations, the Jacobian in JAXFit's trust region algorithm is calculated with automatic differentiation, rather than than using derivative approximations or requiring the user to define the fit function's partial derivatives.
