For a full-column-rank matrix, a thin QR decomposition has , where and is nonsingular upper triangular. Extend to an orthogonal matrix . Orthogonality gives
Thus the unique least-squares solution satisfies , with residual norm . In a rank-deficient problem is singular and uniqueness needs separate treatment.
A Givens rotation is the identity except for the rows/columns , whose block is with . Its transpose times itself has that block equal to the identity, so it is orthogonal. Choose in rows two and three. Then
Back substitution in the first three rows gives
The fourth row of the rotated residual is the unavoidable value , while the other three rows vanish. Thus this rotation also directly exhibits the least-squares optimality.

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