View source for Condition number

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The condition number of a square matrix $A$ is defined as
\begin{equation}
\kappa(A) = \|A\|_2\cdot\|A^{-1}\|_2,
\end{equation}
where $\|\cdot\|_2$ is the spectral norm, that is, the matrix [[norm]] induced by the Euclidean norm of vectors. If A is [[Degenerate_matrix|singular]] then $\kappa(A)=\infty$. In [[numerical analysis]] the condition number of a matrix $A$ is a way of describing how well or badly the system $Ax=b$ could be approximated. If $\kappa(A)$ is small the problem is well-conditioned and if $\kappa(A)$ is large the problem is rather ill-conditioned.

Another expression for the condition number is $\kappa(A) = \dfrac{\sigma_{\max}}{\sigma_{\min}}$, where $\sigma_{\max}$ and $\sigma_{\min}$ are the maximal and minimal singular values of matrix $A$. If $A$ is a symmetric matrix then $\kappa(A)=\left|\dfrac{\lambda_\max}{\lambda_\min}\right|$, where $\lambda_\max$ and $\lambda_\min$ denote the largest and smallest eigenvalues of $A$.