In Nonlinear Least Square Regression, is providing errors from all calculated points to the solver like LMA provide any advantage (in terms of speed and number of iterations) over minimizing the sum of square of errors with a black-box solver? EDITFor the background information refer to this question asked on SO:https://stackoverflow.com/questions/33720645/why-is-this-tensorflow-implementation-vastly-less-successful-than-matlabs-nn
Nonlinear Least Square Regression - Black-Box or LMA Type Methods
optimization;regression;least squares