Non-linear least squares is the form of least squares analysis which is used to fit a set of m observations with a model that is non-linear in n unknown parameters (m > n). It is used in some forms of non-linear regression. The basis of the method is to approximate the model by a linear one and to refine the parameters by successive iterations. There are many similarities to linear least squares, but also some significant differences.
Read more about Non-linear Least Squares: Theory, Initial Parameter Estimates, Multiple Minima, See Also
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—Ezra Pound (1885–1972)