What Is the Nearest Symmetric Matrix?

What is the nearest symmetric matrix to a real, nonsymmetric square matrix A? This question arises whenever a symmetric matrix is approximated by formulas that do not respect the symmetry. For example, a finite difference approximation to a Hessian matrix can be nonsymmetric even though the Hessian is symmetric. In some cases, lack of symmetry is caused by rounding errors. The natural way to symmetrize A is to replace it by (A + A^T)/2. Is this the best choice?

As our criterion of optimality we take that \| A - X \| is minimized over symmetric X for some suitable norm. Fan and Hoffman (1955) showed that (A + A^T)/2 is a solution in any unitarily invariant norm. A norm is unitarily invariant if \|A\| = \|UAV\| for all unitary U and V. Such a norm depends only on the singular values of A, and hence \|A\| = \|A^T\| since A and A^T have the same singular values. The most important examples of unitarily invariant norms are the 2-norm and the Frobenius norm.

The proof that (A+A^T)/2 is optimal is simple. For any symmetric Y,

\notag  \begin{aligned}   \Bigl\| A - \frac{1}{2}(A + A^T) \Bigr\|         &=  \frac{1}{2} \|A - A^T \|          = \frac{1}{2} \| A - Y + Y^T - A^T \| \\         &\le \frac{1}{2} \| A - Y \| + \frac{1}{2} \|(Y - A)^T \| \\         &= \| A - Y\|.   \end{aligned}

Simple examples of a matrix and a nearest symmetric matrix are

\notag    A = \begin{bmatrix} 0 & 1 \\ 0 & 0 \end{bmatrix}, \;\;    X = \begin{bmatrix} 0 & \frac{1}{2} \\ \frac{1}{2} & 0 \end{bmatrix},\qquad    A = \begin{bmatrix} 0 & 1 \\ -1 & 0 \end{bmatrix}, \;\;    X = \begin{bmatrix} 0 & 0 \\ 0 & 0 \end{bmatrix}.

Note that any A can be written

\notag   A = \frac{1}{2} (A + A^T ) + \frac{1}{2} (A - A^T )  \equiv B + C,

where B and C are the symmetric part and the skew-symmetric part of A, respectively, so the nearest symmetric matrix to A is the symmetric part of A.

For the Frobenius norm, (A + A^T)/2 is the unique nearest symmetric matrix, which follows from the fact that \|S + K\|_F^2 = \|S\|_F^2 + \|K\|_F^2 for symmetric S and skew-symmetric K. For the 2-norm, however, the nearest symmetric matrix is not unique in general. An example of non-uniqueness is

\notag    A = \begin{bmatrix} 0 & 0 & 0 \\ 0 & 0 & 1 \\ 0 & 0 & 0 \end{bmatrix}, \quad    X = \begin{bmatrix} 0 & 0 & 0 \\ 0 & 0 & \frac{1}{2} \\ 0 & \frac{1}{2} & 0 \end{bmatrix}, \quad    Y = \begin{bmatrix} \theta & 0 & 0 \\ 0 & 0 & \frac{1}{2} \\ 0 & \frac{1}{2} & 0 \end{bmatrix},

for which \|A - X\|_2 = 0.5, and \|A - Y\|_2 = 0.5 for any \theta such that |\theta| \le 0.5.

Entirely analogous to the above is the nearest skew-symmetric matrix problem, for which the solution is the skew-symmetric part for any unitarily invariant norm. For complex matrices, these results generalize in the obvious way: (A + A^*)/2 is the nearest Hermitian matrix to A and (A - A^*)/2 is the nearest skew-Hermitian matrix to A in any unitarily invariant norm.

Reference

This article is part of the “What Is” series, available from https://nhigham.com/category/what-is and in PDF form from the GitHub repository https://github.com/higham/what-is.

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