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A Givens rotation eliminates one component of a vector by rotating just two coordinates, preserving its Euclidean norm. For example,The length remains five: elimination moves the component into the retained coordinate rather than discarding it. Applied to a matrix, the same operation mixes two rows; QR decomposition by Givens rotations uses these local operations to build an upper triangular matrix.

For indices $i<j$, the matrix $G_{ij}(c,s)$ is the identity matrix except for its block on coordinates $i,j$:This article uses left matrix multiplication on column vectors. If $c=\cos \theta$ and $s=\sin \theta$, the block is a rotation matrix through angle $-\theta$: the minus sign belongs in the lower left. Its transpose reverses the rotation. Direct multiplication gives $G^{T}G=I$ and determinant one, so it is an orthogonal matrix; consequently $\Vert Gx{\Vert }_2^2=x^T{G}^{T}Gx=\Vert x{\Vert }_2^2$. Only two coordinates change, but this Euclidean norm identity holds for the entire vector.

Left matrix multiplication by a Givens rotation replaces rows $i,j$ of a matrix byBoth right-hand sides use the old entries. An in-place algorithm must save the first result temporarily, update the second entry, and only then overwrite the first. The reference BLAS DROT implementation follows this order with its temporary DTEMP.^[ref]

For elimination in column $k$, construct $(c,s)$ from $(A_{ik},A_{jk})$. This sets the lower component to zero in exact arithmetic. In floating-point arithmetic the computed expression can leave a small remainder; after computing the other updated entries, explicitly store $A_{jk}^{\prime }=0$ and $A_{ik}^{\prime }=r$. Previously eliminated columns stay zero if both participating rows are already zero there. This is why the ordering in QR decomposition by Givens rotations matters.

A QR decomposition of an $m\times n$ matrix, $m\ge n$, is $A=QR$, where $Q$ is an $m\times m$ orthogonal matrix and $R$ is $m\times n$ and upper trapezoidal: $R_{ij}=0$ for $i>j$. In particular its bottom $m-n$ rows vanish.

Successive left Givens rotations annihilate the entries below the diagonal, column by column. When working on column $j$, rotate rows $j$ and $i>j$; their earlier columns are already zero, so the earlier eliminations persist. If $G_k\cdots G_{1}A=R$, thenA product of orthogonal matrices is orthogonal. The construction remains valid for a rank-deficient matrix, although uniqueness is not asserted. This is QR decomposition by Givens rotations.

A Givens rotation ${\Omega }^{[p,q]}$ is the identity except in rows and columns $p,q$, where it has the blockFor $a=A_{pj}$ and $b=A_{qj}$, choosewhen $(a,b)\ne (0,0)$. ThenIf both entries vanish, any angle works.

For the given matrix, first useIt givesThen useThus $R={\Omega }^{[2,3]}{\Omega }^{[1,3]}A$ isAll leading row entries are positive. The resulting QR decomposition by Givens rotations iswhereand $R$ is the matrix above. Since it is a product of transposed rotations, $Q$ is orthogonal.

Put $C=Q_{1}A$ and write its second column as $v$. For $n\ge 3$, takewhere each angle is computed from the current second column, in the right-to-left application order. These Givens rotations eliminate its entries in rows $3,\dots ,n$ by accumulating their Euclidean norm in row two.

Each rotation acts only on rows with indices at least two. The first column of $C$ is $\Vert a{\Vert }_2{e}_1$, whose entries in all those rows are zero. Mixing two zeros leaves them zero, and row one is untouched. Thus the first column is unchanged. The resulting matrix has the formThis verifies both required properties, including the case of zero second-column tail. When $n=2$ there are no rows to eliminate: take $Q_2=I_2$, the empty product. No sign change of $v_2$ is required in that case. Each Givens row-pair update preserves previously eliminated columns, which is the central invariant of QR decomposition by Givens rotations.

Apply the product from right to left, choosing every Givens rotation from the current first column. First $J(1,2)$ replaces $(a_1,a_2)$ by $(\sqrt{a_1^2+a_2^2},0)$. Next $J(1,3)$ combines that retained entry with $a_3$ and sets the third entry to zero. Inductively, after eliminating coordinates $2,\dots ,k$, the first coordinate isand coordinates $2,\dots ,k$ are zero. Later Givens rotations involve only coordinate one and a new coordinate, so those zeros remain zero. Thereforewhere $e_1$ is the first standard basis vector. The zero vector is included by choosing the identity matrix for every zero pair. The nonnegative sign follows from the convention fixed in part (c)(ii). This is the first-column stage of QR decomposition by Givens rotations.

A real square orthogonal matrix satisfies $Q^{T}Q=Q{Q}^T=I$, so it preserves the Euclidean norm. The columns of $Z_h$ are orthonormal, hence $\Vert Z_{h}v{\Vert }_2=\Vert v{\Vert }_2$. The induced matrix norm therefore givesFor the left truncation, $\Vert Q_{\ell }^{T}A{\Vert }_2\le \Vert A{\Vert }_2$, and equality is guaranteed for arbitrary $A$ only when $\boxed{\ell =m}$. Indeed, if $\ell <m$, take $A=q_{\ell +1}{e}_1^T$, where $q_{\ell +1}$ is an omitted column of $Q$. Then $\Vert A{\Vert }_2=1$ but $Q_{\ell }^{T}A=0$. Merely taking $\ell \ge n$ does not guarantee preservation for an arbitrary $A$.

A full QR decomposition of a real $m\times n$ matrix is $A=QR$, with $Q$ orthogonal and $R$ upper trapezoidal; for a square matrix, $R$ is upper triangular. Fix the following sign convention for a Givens rotation: it is the identity except for its $(i,k)$ blockThe block transpose times the block is $I_2$ because $c^2+s^2=1$, proving orthogonality. Writing $r=\sqrt{x_i^2+x_k^2}$, the choice $\boxed{c=x_i/r,s=x_k/r}$ gives $(Jx{)}_k=0$ and $(Jx{)}_i=r$. To zero the other coordinate take $\boxed{c=x_k/r,s=-x_i/r}$, giving $(Jx{)}_i=0$. If $r=0$, both entries are already zero and the identity suffices.

For a tridiagonal matrix, successively rotate adjacent row pairs $(1,2),(2,3),\dots ,(n-1,n)$, each time zeroing the current entry $(k+1,k)$. Earlier columns remain triangular because both affected rows are already zero there. The result $R=J_{n-1}\cdots J_{1}A$ is upper triangular, so $Q=J_1^T\cdots J_{n-1}^T$ gives its QR decomposition. Only $n-1$ rotations are required; the resulting $R$ has at most two superdiagonals.

For a full square matrix, process columns $j=1,\dots ,n-1$. Within column $j$, for $i=n,n-1,\dots ,j+1$, rotate rows $(i-1,i)$ to eliminate the current $(i,j)$ entry. Previously cleared columns stay clear. With $J_1,\dots ,J_s$ in application order, $s=n(n-1)/2$, one has $\boxed{R=J_s\cdots J_{1}A,Q=J_1^T\cdots J_s^T}$. This is QR decomposition by Givens rotations.