Ronald DeVore
Title: Decoders
for Compressed Sensing
Abstract: In
compressed sensing, we encode a discrete signal $x\in \R^N$ by the
vector $y=\Phi x$ where $\Phi$ is a suitably chosen $n\times N$
matrix with $n<<N$. The vector $x$ is underdetermined by
$y$ and so decoding $y$ is a typical inverse problem. We shall
discuss various ways of decoding $y$ including $\ell_1$ minimization
and greedy algorithms. We shall discuss the performance of
encoder-decoder pairs in terms of accuracy for a given computational
budget.