Bounds for the Matrix Condition Number

We present a selection of bounds for the condition number \kappa(A) = \|A\| \|A^{-1}\| of a nonsingular matrix A\in\mathbb{C}^{n\times n} in terms of quantities that might be known or can be estimated.

General Matrices

From the inequality \|A\| \ge \rho(A), for any matrix norm, where \rho(A) is the spectral radius (the largest magnitude of any eigenvalue of A) we have

\notag       \kappa(A) \ge \rho(A) \rho(A^{-1}).  \qquad (1)

Fir the 2-norm, this bound is an equality for a normal matrix (one for which A^*A = AA^*), but it can be arbitrarily weak for nonnormal matrices.

Guggenheimer, Edelman, and Johnson (1995) obtain the bound

\notag       \kappa_2(A) < \displaystyle\frac{2}{|\det(A)|}                  \left( \frac{\|A\|_F}{n^{1/2}} \right)^n. \qquad (2)

The proof of the bound applies the arithmetic–geometric mean inequality to the n numbers \sigma_1^2/2, \sigma_1^2/2,  \sigma_2^2, \sigma_3^2, \dots, \sigma_{n-1}^2, where the \sigma_i are the singular values of A. This bound can be arbitrarily weak but it is an approximate equality when \sigma_1,\sigma_2, \dots \sigma_{n-1} are of similar order of magnitude.

Merikoski, Urpala, Virtanen, Tam, and Uhlig (1997) obtain the bound

\notag  \kappa_2(A) \le  \left(\displaystyle\frac{1+x}{1-x}\right)^{1/2}, \quad      x = \sqrt{1 - (n/\|A\|_F^2)^n |\det(A)|^2 }. \qquad (3)

Their proof uses a more refined application of the arithmetic–geometric mean inequality, and they show that this bound is the smallest that can be obtained based on \|A\|_F, \det(A), and n only. Hence (3) is no larger than (2), and they show that it can be smaller by no more than 1.5. Equality holds in (3) if and only if \sigma_2 = \sigma_3 = \cdots = \sigma_{n-1} = (\sigma_1 + \sigma_n)/2.

As an example, for three random 25\times 25 matrices with \kappa_2(A) = 10, generated by gallery('randsvd') with three different singular value dsitributions:

Mode (2) (3)
One large singular value 9.88e+07 9.88e+07
One small singular value 1.21e+01 1.20e+01
Geometrically distributed singular values 5.71e+04 5.71e+04

We note that for larger \kappa_2(A) the formula (3) is prone to overflow, which can be avoided by evaluating it in higher precision arithmetic.

Hermitian Positive Definite Matrices

Merikoski et al. (1997) also give a version of (3) for Hermitian positive definite A\in\mathbb{C}^{n\times n}:

\kappa_2(A) \le \displaystyle\frac{1+x}{1-x}, \quad      x = \sqrt{1 - (n/\mathrm{trace}(A))^n \det(A) }.     \qquad (4)

This is the smallest bound that can be obtained based on \mathrm{trace}(A), \det(A), and n only. Equality holds in (4) if and only if the eigenvalues \lambda_1 \ge \lambda_2 \ge \cdots \ge \lambda_n of A satisfy \lambda_2 = \lambda_3 = \cdots = \lambda_{n-1} = (\lambda_1 + \lambda_n)/2. We can rewrite this upper bound as

\displaystyle\frac{1+x}{1-x} = \frac{(1+x)^2}{1-x^2}                < \frac{4}{1-x^2},

which gives the weaker bound

\notag   \kappa_2(A) < \displaystyle\frac{4}{\det(A)} \Bigl(\displaystyle\frac{\mathrm{trace}(A)}{n}\Bigr)^n.     \qquad (5)

This bound is analogous to (2) and is up to a factor 4 larger than (4), this factor being attained for A = I.

If \mathrm{trace}(A) = n then (4) reduces to

\notag \begin{aligned}   \kappa_2(A) &< \displaystyle\frac{1 + \sqrt{1-\det(A)}}{1 - \sqrt{1-\det(A)}}                =\displaystyle\frac{\bigl(1 + \sqrt{1-\det(A)}\,\bigr)^2}{\det(A)}   \qquad(6)\\               &< \displaystyle\frac{4}{\det(A)}. \end{aligned}

These bounds hold for any positive definite matrix with unit diagonal, that is, any nonsingular correlation matrix.

We can sometimes get a sharper bound than (4) and (5) by writing A = DCD, where D = \mathrm{diag}(a_{ii}^{1/2}) and c_{ii} \equiv 1 (thus C is a correlation matrix), using

\notag \kappa_2(A) \le \kappa_2(D)^2 \kappa_2(C)           = \displaystyle\frac{\max_i a_{ii}}{\min_i a_{ii}} \kappa_2(C), \qquad (7)

and bounding \kappa_(C) using (6). For example, for the 5\times 5 Pascal matrix

\notag P_5 = \left[\begin{array}{ccccc} 1 & 1 & 1 & 1 & 1\\ 1 & 2 & 3 & 4 & 5\\ 1 & 3 & 6 & 10 & 15\\ 1 & 4 & 10 & 20 & 35\\ 1 & 5 & 15 & 35 & 70 \end{array}\right]

the condition number is \kappa_1(P_5) = 8.52 \times 10^3. The bounds from (4) and (5) are both 1.22 \times 10^7, whereas combining (4) and (7) gives a bound of 4.70 \times 10^6.


Many other condition number bounds are available in the literature. All have their pros and cons and any bound based on limited information such as traces of powers of A and the determinant will be potentially very weak.

A drawback of the bounds (3)–(6) is that they require \det(A). Sometimes the determinant is easily computable, as for a Vandermonde matrix, or can be bounded: for example, |\det(A)| \ge 1 for a matrix with integer entries. If a Cholesky, LU, or QR factorization of A is available then |\det(A)| is easily computable, but in this case a good order of magnitude estimate of the condition number can be cheaply computed using condition estimation techniques (Higham, 2002, Chapter 15).

The bounds (3) and (4) are used by Higham and Lettington (2021) in investigating the most ill conditioned 4\times 4 symmetric matrices with integer elements bounded by 10; see What Is the Wilson Matrix?


This is a minimal set of references, which contain further useful references within.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s