Let , using the real sign function on the support of a vector . The fixed-sign null space condition in the PDF uses on its right-hand side. This complement is essential. For any real coordinate , the supporting-line inequality for the absolute value is
Every distinct feasible vector is with . Summing the coordinate inequalities on and adding the L1 norm on gives
The strict final inequality is precisely the fixed-sign null space condition. Hence is the unique basis pursuit minimizer. Unlike the null space property of Question 1, this condition concerns the particular sign function values of , rather than every vector supported in .
Assume is the unique basis pursuit minimizer. Fix and set
There is such that the sign function of equals that of at every whenever . To choose it, take less than the minimum of over the nonzero in ; if that set is empty, any positive works. On this interval the L1 norm has the exact expression
For , both and are distinct feasible vectors. Uniqueness forces their objective differences to be strictly positive, so and . Therefore
The fixed-sign null space condition is necessary as well as sufficient. This argument also covers an empty support of a vector: then and the nonzero null space vector has . Strictness is indispensable: equality would make a sufficiently short feasible segment have the same objective as .
Let be the strict dual certificate for basis pursuit supplied by condition (ii). For , we have
Here the adjoint operator is the real transpose. The injectivity of ensures : otherwise would force . Thus at least one nonzero term lies outside the support of a vector . Since at every such index,
This proves the fixed-sign null space condition, so part (a) gives uniqueness in basis pursuit. If is empty, the injectivity of instead means there is no nonzero null space vector, and the feasible set is a singleton. Injective active columns and a strict dual certificate for basis pursuit ensure unique recovery. Both ingredients matter: strictness outside cannot detect a nonzero null space direction supported entirely inside .
Under the usual definition, the null space property relative to is for every nonzero . It characterizes recovery by basis pursuit of every vector supported in . Indeed, if it holds, then for any such and any nonzero ,
Conversely, failure for some makes and feasible with the same measurements and . Thus
For the single fixed vector in the printing, necessity is false with that definition. For example,
has . Along its feasible line, the objective is , uniquely minimized at , although the null space property would require .
The correct fixed-sign null space condition is
Sufficiency follows from the supporting-line inequality for the absolute value. Necessity follows by considering for small positive , when all signs on remain unchanged: a nonpositive directional increase gives either a decrease or another minimizer. If “relative to ” was intended to include these signs, this is the precise condition needed.
For a real vector with support of a vector , this certificate is a vector whose measurements under the adjoint operator match the active sign function values and lie strictly between minus one and one on the inactive coordinates. If is injective, such a certificate proves that is the unique basis pursuit minimizer. For a nonzero , injectivity ensures , and implies the fixed-sign null space condition. The strict inequality is interpreted coordinatewise if is empty. The condition with the sign function written here is for real variables; complex basis pursuit uses the unit phases of active coordinates instead.