uniqueness of a sparse solution
Let be a field, and let We denote by the Hamming weight of a column vector i.e., Consider a non-negative integer and an matrix whose entries belong to
Theorem 1 (Donoho and Elad)
The following conditions are equivalent:
(1)
for each column vector there exists at most one such that and
(2)
Proof. First, suppose that condition (1) is not satisfied. Then there exist column vectors such that and Consequently, and Moreover, by the definition of the Hamming weight, 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 |