两者之间的区别西皮·莱斯特斯以及最小平方法

Question

我想知道^{}和{a2}这两种方法有什么区别？在

当我实施它们时，它们产生的chi^2差异最小：

>>> solution0 = ((p0.fun).reshape(100,100))
>>> # p0.fun are the residuals of my fit function np.ravel'ed as returned by least_squares
>>> print(np.sum(np.square(solution0))) 
0.542899505806

>>> solution1 = np.square((median-solution1))
>>> # solution1 is the solution found by least_sq, it does not yield residuals thus I have to subtract it from the median to get the residuals (my special case)
>>> print(np.sum((solution1)))
0.54402852325

有谁能详细说明一下，或者指出我在哪里可以找到其他的文档，scipy的文档有点神秘。在

Answer 1

从least_squares的文档来看，leastsq似乎是一个旧的包装器。在

See also

leastsq   A legacy wrapper for the MINPACK implementation of the Levenberg-Marquadt algorithm.

所以您应该使用least_squares。似乎least_squares具有其他功能。其中最重要的是使用的默认“方法”（即算法）不同：

• trf : Trust Region Reflective algorithm, particularly suitable for large sparse problems with bounds. Generally robust method.
• dogbox : dogleg algorithm with rectangular trust regions, typical use case is small problems with bounds. Not recommended for problems with rank-deficient Jacobian.
• lm : Levenberg-Marquardt algorithm as implemented in MINPACK. Doesn’t handle bounds and sparse Jacobians. Usually the most efficient method for small unconstrained problems.

Default is trf. See Notes for more information.

旧的leastsq算法只是lm方法的包装器，正如文档所说，它只适用于小的无约束问题。在

您在结果中看到的差异可能是由于所采用的算法的不同。在