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Alternating right and left Householder transformations reduce a real square matrix to a bidiagonal matrix. For a lower-bidiagonal result, first clear row $j$ beyond column $j$ using a right transformation on trailing columns. Then clear column $j$ below row $j+1$ using a left transformation on trailing rows. Previously completed rows and columns are preserved. Since $B^{T}B=Q^T{A}^{T}AQ$ is a symmetric tridiagonal matrix, its eigenvalues are the squares of the singular values of $A$. This explains the reduction structurally; explicitly forming $A^{T}A$ is unnecessary and can worsen numerical conditioning.

The singular value decomposition $A=U\Sigma V^T$ givesTherefore $\boxed{{\sigma }_i(A)=\sqrt{{\lambda }_i(A^{T}A)}}$, counting multiplicities. These are the nonnegative square roots: zero singular values must be included if $A$ is singular. Strict positivity requires full rank, which the question does not assume.

Write the lower bidiagonal matrix entries as $B_{ii}=d_i$ and $B_{i+1,i}=e_i$. Its columns $i$ and $j$ have disjoint supports if $|i-j|>1$, so $T=B^{T}B$ has no entries outside the three central diagonals. More explicitly,This proves both symmetry and the tridiagonal matrix structure.

Use banded Gaussian elimination followed by back substitution. To distinguish the right-hand side from the upper-diagonal coefficients $b_i$, write it as $r_i$. If the modified pivot elements are nonzero, the tridiagonal elimination recurrences areThey eliminate just the one entry below each pivot element. Solve the resulting upper bidiagonal matrix system byThere is a bounded number of arithmetic operations per row in each sweep, giving $O(n)$ arithmetic operations and $O(n)$ storage. The unpivoted recurrences cost $8n-7$ arithmetic operations under the usual scalar-operation count.

For a general tridiagonal matrix, nonsingularity alone does not guarantee these pivot elements. If a zero or unsafe pivot element occurs, use partial pivoting between the current row and the next row. These are the only possible nonzero candidates in the current column; adjacent row swaps introduce at most a second upper diagonal. Tracking that fill-in keeps both Gaussian elimination and back substitution at $O(n)$ arithmetic operations. For example, $\left(\begin{array}{cc}0& 1\\ 1& 0\end{array}\right)$ is invertible but requires a row swap. If the input is singular, report a zero final pivot element; a unique solution cannot be promised, and compatibility or free-variable analysis is a separate issue. No nonsingularity or safe-pivot assumption is silently inferred from the word tridiagonal.

Let $n\ge 1$. Store the tridiagonal matrix $A$ by its diagonal $b_1,\dots ,b_n$, lower diagonal $a_2,\dots ,a_n$, and upper diagonal $c_1,\dots ,c_{n-1}$. The linear system $Ax=d$ iswith the nonexistent endpoint terms omitted. Initialize $p_1=b_1$ and $g_1=d_1$. For $i=2,\dots ,n$, compute the elimination multiplier, modified pivot element, and modified right-hand side:These recurrence relations apply the elementary row operation $R_i\leftarrow R_i-m_i{R}_{i-1}$ after the preceding row has already been reduced. The transformed linear system is an upper bidiagonal matrix:Thus back substitution is especially short:For $n=1$, only $x_1=d_1/b_1$ remains. Check each pivot element before its first division, including $p_n$ before back substitution.

Equivalently, this constructs an LU decomposition with unit lower bidiagonal matrix $L$, whose lower diagonal is $m_2,\dots ,m_n$, and upper bidiagonal matrix $U$, whose diagonal is $p_1,\dots ,p_n$ and upper diagonal is $c_1,\dots ,c_{n-1}$. The $g$ sweep is forward substitution in $Lg=d$. There is no fill-in outside these bands when no rows are interchanged. LAPACK's DGTSV source uses these same coefficient and right-hand-side updates in its branch without row interchange.^[ref]