Restricted isometry property

In linear algebra, the restricted isometry property characterizes matrices which are nearly orthonormal, at least when operating on sparse vectors. The concept was introduced by Emmanuel Candès and Terence Tao and is used to prove many theorems in the field of compressed sensing. There are no known large matrices with bounded restricted isometry constants (computing these constants is strongly NP-hard, and is hard to approximate as well), but many random matrices have been shown to remain bounded. In particular, it has been shown that with exponentially high probability, random Gaussian, Bernoulli, and partial Fourier matrices satisfy the RIP with number of measurements nearly linear in the sparsity level. The current smallest upper bounds for any large rectangular matrices are for those of Gaussian matrices. Web forms to evaluate bounds for the Gaussian ensemble are available at the Edinburgh Compressed Sensing RIC page.

Let A be an m × p matrix and let 1 ≤ s ≤ p be an integer. Suppose that there exists a constant ${\displaystyle \delta _{s}\in (0,1)}$ such that, for every m × s submatrix A_s of A and for every s-sparsity vector y,

Then, the matrix A is said to satisfy the s-restricted isometry property with restricted isometry constant ${\displaystyle \delta _{s}}$.

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