1,553 research outputs found
Explicit constructions of RIP matrices and related problems
We give a new explicit construction of matrices satisfying the
Restricted Isometry Property (RIP). Namely, for some c>0, large N and any n
satisfying N^{1-c} < n < N, we construct RIP matrices of order k^{1/2+c}. This
overcomes the natural barrier k=O(n^{1/2}) for proofs based on small coherence,
which are used in all previous explicit constructions of RIP matrices. Key
ingredients in our proof are new estimates for sumsets in product sets and for
exponential sums with the products of sets possessing special additive
structure. We also give a construction of sets of n complex numbers whose k-th
moments are uniformly small for 1\le k\le N (Turan's power sum problem), which
improves upon known explicit constructions when (\log N)^{1+o(1)} \le n\le
(\log N)^{4+o(1)}. This latter construction produces elementary explicit
examples of n by N matrices that satisfy RIP and whose columns constitute a new
spherical code; for those problems the parameters closely match those of
existing constructions in the range (\log N)^{1+o(1)} \le n\le (\log
N)^{5/2+o(1)}.Comment: v3. Minor correction
Computational Complexity of Certifying Restricted Isometry Property
Given a matrix with rows, a number , and , is
-RIP (Restricted Isometry Property) if, for any vector , with at most non-zero co-ordinates, In many applications, such as
compressed sensing and sparse recovery, it is desirable to construct RIP
matrices with a large and a small . Given the efficacy of random
constructions in generating useful RIP matrices, the problem of certifying the
RIP parameters of a matrix has become important.
In this paper, we prove that it is hard to approximate the RIP parameters of
a matrix assuming the Small-Set-Expansion-Hypothesis. Specifically, we prove
that for any arbitrarily large constant and any arbitrarily small
constant , there exists some such that given a matrix , it
is SSE-Hard to distinguish the following two cases:
- (Highly RIP) is -RIP.
- (Far away from RIP) is not -RIP.
Most of the previous results on the topic of hardness of RIP certification
only hold for certification when . In practice, it is of interest
to understand the complexity of certifying a matrix with being close
to , as it suffices for many real applications to have matrices
with . Our hardness result holds for any constant
. Specifically, our result proves that even if is indeed very
small, i.e. the matrix is in fact \emph{strongly RIP}, certifying that the
matrix exhibits \emph{weak RIP} itself is SSE-Hard.
In order to prove the hardness result, we prove a variant of the Cheeger's
Inequality for sparse vectors
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