The null space property relative to is for every nonzero . It is equivalent to exact basis pursuit recovery of every vector supported in . Sufficiency follows from the triangle inequality; for necessity, compare and , which have the same image under . Recovery of a single fixed signed vector can hold without this uniform property.
For , this strict inequality for every nonzero and every is equivalent to unique constrained recovery of every sparse vector of order . For sufficiency, use on the support of a vector. For necessity compare with , which have the same measurements. The statement concerns uniform recovery over the whole sparse class, rather than a single signed vector.
If the Lq null space property holds at , it holds at every . Order a nonzero null vector's magnitudes and put . Since , the top- -power sum is at most times its -power sum, while the tail -power sum is at least that multiple of the tail -power sum. The strict inequality therefore gives the strict inequality. The largest magnitudes are the worst support of a vector, so all other supports satisfy it too.
The displayed property holds for every and every , with and . It converts the cone inequality from basis pursuit minimality and the tube inequality from noisy feasibility into a two-constant bound . Both constants are needed in general; a fixed coefficient one on the approximation term does not follow.
For a real vector with support , uniqueness in basis pursuit is equivalent to for every nonzero . The supporting-line inequality for the absolute value proves sufficiency. Taking small positive and negative multiples of before any active sign changes proves necessity.
Articles by others on the same topic
The Nullspace Property (NSP) is a concept in the field of convex optimization, particularly in relation to the formulation of certain convex problems, such as basis pursuit and sparse representation. It is closely associated with matrices and their structure in terms of representing linear systems.