uniqueness of a sparse solution
Let 𝔽 be a field, and let m,n∈ℕ∖{0}. We denote by ∥x∥0 the Hamming weight of a column vector x=[x1,…,xn]T∈𝔽n, i.e., ∥x∥0=#{j∈{1,…,n}:xj≠0}. Consider a non-negative integer k, and an m×n matrix A whose entries belong to 𝔽.
Theorem 1 (Donoho and Elad)
The following conditions are equivalent:
(1)
for each column vector y∈Fm there exists at most one x∈Fn such that Ax=y and ∥x∥0≤k,
(2)
k<12spark(A).
Proof. First, suppose that condition (1) is not satisfied. Then there exist column vectors v,w∈𝔽n such that v≠w,Av=Aw, and max{∥v∥0,∥w∥0}≤k. Consequently, v-w∈𝔽n∖{𝟎} and A(v-w)=𝟎. Moreover, by the definition of the Hamming weight, ∥v-w∥0≤2k. Thus, which means that condition (2) is not satisfied.
Next, suppose that (2) is not satisfied. Then there exists a column vector such that and It is easy to see that for some with (If and define by
If define Since and condition (1) is not satisfied.
References
- 1 D. L. Donoho and M. Elad, Optimally sparse representation in general (nonorthogonal) dictionaries via minimization, Proc. Natl. Acad. Sci. USA 100, No. 5: 2197–2202 (2003).
- 2 Compressed Sensing: Theory and Applications, edited by Y. C. Eldar and G. Kutyniok, Cambridge University Press, 2012.
Title | uniqueness of a sparse solution |
---|---|
Canonical name | UniquenessOfASparseSolution |
Date of creation | 2013-03-22 19:36:26 |
Last modified on | 2013-03-22 19:36:26 |
Owner | kammerer (26336) |
Last modified by | kammerer (26336) |
Numerical id | 54 |
Author | kammerer (26336) |
Entry type | Theorem |
Classification | msc 15A06 |