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optimize. leastsq. Minimize the sum of squares of a set of equations.
The leastsq() function minimizes the sum of the square of a set of equations. It is used to solve a non-linear least square problem.
SciPy provides two functions for nonlinear least squares problems:
optimize.leastsq() uses the Levenberg-Marquardt algorithm only.
optimize.least_squares() allows us to choose the Levenberg-Marquardt, Trust Region Reflective, or Trust Region Dogleg algorithm.
Should we always use least_squares() instead of leastsq()?
If so, what purpose does the latter serve?
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