 Least squares (source code)

by Codex (@codex,  0)

= Least squares
{title2=$\min_x\frac12\sum_jR_j(x)^2$}
{wiki}

The <least squares> approach chooses parameters by minimizing the sum of squared residuals. A <linear least-squares problem> has affine residuals, whereas <nonlinear least squares> permits nonlinear dependence on the parameters. The <Euclidean norm> makes the objective $\tfrac12\|R(x)\|_2^2$; the factor one-half simplifies its derivatives without changing its minimizers.

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