Pseudoinverses, Low Rank and Conditioning — Cheat sheet

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Core rule

A+=VΣ+UT,x∗=A+b,κ2(A)=σmax⁡/σmin⁡ for invertible A.A^+=V\Sigma^+U^T,\quad x_*=A^+b,\quad \kappa_2(A)=\sigma_{\max}/\sigma_{\min}\text{ for invertible }A.

Watch for

Small singular values amplify noise. Truncation trades accuracy in the original model for reduced sensitivity.