A full QR decomposition of a real by matrix with writes
where is an orthogonal by matrix and is upper triangular by . In the full-column-rank case is invertible. Equivalently the reduced factorization is with . Householder transformations can construct the full factorization.
Let with . Since orthogonal transformations preserve the Euclidean norm,
Therefore the linear least-squares problem has the full-rank solution
Back substitution solves this triangular system without forming . If the matrix is rank deficient, use a rank-revealing pivoted factorization and minimize over its independent columns; the minimizer need not be unique. The original PDF minimizes over ; the converted TeX's minimization over is a transcription error.

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