# Mutual coherence (linear algebra)

In linear algebra, the coherence[1] or mutual coherence[2] of a matrix A is defined as the maximum absolute value of the cross-correlations between the columns of A.

Formally, let ${\displaystyle a_{1},\ldots ,a_{m}\in {\mathbb {C} }^{d}}$ be the columns of the matrix A, which are assumed to be normalized such that ${\displaystyle a_{i}^{H}a_{i}=1.}$ The mutual coherence of A is then defined as[1][2]

${\displaystyle M=\max _{1\leq i\neq j\leq m}\left|a_{i}^{H}a_{j}\right|.}$

A lower bound is [3]

${\displaystyle M\geq {\sqrt {\frac {m-d}{d(m-1)}}}}$

A deterministic matrix with the mutual coherence almost meeting the lower bound can be constructed by Weil's theorem.[4]

The concept was introduced in a slightly less general framework by David Donoho and Xiaoming Huo,[5] and has since been used extensively in the field of sparse representations of signals. In particular, it is used as a measure of the ability of suboptimal algorithms such as matching pursuit and basis pursuit to correctly identify the true representation of a sparse signal.[1][2][6]