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Compressed sensing reconstructs a sparse vector, or an approximately sparse one, from fewer linear measurements than its ambient dimension. Basis pursuit replaces counting nonzero coordinates by convex ${\ell }^1$ minimization. A null space property characterizes exact uniform recovery, while a robust null space property controls errors from noise and nonsparse tails.

Let $T$ be the support of a vector $\hat{x}$. If the columns indexed by $T$ were linearly dependent, there would be a nonzero $h$ supported in $T$ with $Ah=0$. For sufficiently small real $t$, the nonzero signs of ${\hat{x}}_T$ remain fixed, soBoth positive and negative $t$ preserve the residual and hence feasibility, even with noise. A nonzero slope contradicts minimality in one direction. A zero slope gives distinct minimizers, contradicting uniqueness. Thus those columns are linearly independent, andThis proves the sparse vector assertion without assuming a noiseless residual.

The restricted isometry property controls $\Vert Ax{\Vert }_2^2/\Vert x{\Vert }_2^2$ uniformly over sparse vectors. It supplies quantitative near-orthogonality of disjoint sparse coordinate combinations, not merely normalization of individual columns.