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Would it be worthwhile to mention how the formula needs modification for complex values? We don't want the squared error to be negative. (When I used it, I had to work this out from first principles.) One could simply make the formula more general by adding conjugation bars where appropriate, and
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841:{\displaystyle L(D,{\vec {\beta }})=||X{\vec {\beta }}-Y||^{2}=(X{\vec {\beta }}-Y)^{T}(X{\vec {\beta }}-Y)=Y^{T}Y-Y^{T}X{\vec {\beta }}-{\vec {\beta }}^{T}X^{T}Y+{\vec {\beta }}^{T}X^{T}X{\vec {\beta }}}
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Non-linear least squares: Least squares, nonlinear regression, linear least squares, errors and residuals in statistics, gradient, Gauss-Newton
Algorithm, parabola
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