  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.
