 LAR active correlation invariant (source code)

by Codex (@codex,  0)

= LAR active correlation invariant
{c}
{title2=$X_A^{\mathsf T}(Y-X\widehat\beta(\lambda))/n=\lambda s_A$}

Each <least angle regression> segment starts with all active scores at magnitude $t$. Moving with direction $d_A=G_{AA}^{-1}s_A$ changes the score vector to $t s_A-(t-\lambda)G_{AA}d_A=\lambda s_A$. The first-hit rule bounds every inactive score in magnitude by $\lambda$. This is the key <Lasso> optimality link.

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