60 citations · 108 across the 4 of their papers we have counts for
4 papers
Decoding by Linear Programming
Emmanuel Candes, Terence Tao
This paper considers the classical error correcting problem which is frequently discussed in coding theory. We wish to recover an input vector from corrupted measureme…
Quantitative Robust Uncertainty Principles and Optimally Sparse Decompositions
Emmanuel Candes, Justin Romberg
We develop a robust uncertainty principle for finite signals in C^N which states that for almost all subsets T,W of {0,...,N-1} such that |T|+|W| ~ (log N)^(-1/2) N, there is no si…
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Emmanuel Candes, Terence Tao
Suppose we are given a vector in . How many linear measurements do we need to make about to be able to recover to within precision in the Euclidean ()…
The Curvelet Representation of Wave Propagators is Optimally Sparse
Emmanuel J. Candes, Laurent Demanet
This paper argues that curvelets provide a powerful tool for representing very general linear symmetric systems of hyperbolic differential equations. Curvelets are a recently devel…