Past exam of the mathematics course of the University of Cambridge / 2013 / iii / Paper 36 / 2 / b

Use the natural empty-sum convention ${\mu }_1(0)=0$ for cumulative coherence. This is needed for the printed case $s=1$. For $x=0$ the conclusion is immediate. Otherwise let $S$ be its support of a vector, with $k=|S|\le s$. The Gram matrix $G=A_S^{\ast }{A}_S$ has diagonal entries one because the columns have unit Euclidean norm. Each off-diagonal row sum is bounded byThe last inequality uses monotonicity of cumulative coherence: enlarging an index set only adds nonnegative summands. There are enough indices to enlarge it because $s\le N$.

The Gershgorin circle theorem places every eigenvalue of $G$ within distance ${\mu }_1(s-1)$ of one. Since $G$ is a Hermitian matrix, its eigenvalues are real, and the finite-dimensional spectral theorem givesThis is the cumulative coherence bound for restricted isometry. The lower bound remains valid when ${\mu }_1(s-1)>1$, although it is then nonpositive. No assumption that the matrix is already a near isometry is required. The distortion is bounded by ${\mu }_1(s-1)$ on every order-$s$ sparse vector.