# Technical Program

## Paper Detail

 Paper ID G.3.1 Paper Title Optimal Restricted Isometry Condition for Exact Sparse Recovery with Orthogonal Least Squares Authors Junhan Kim, Byonghyo Shim, Seoul National University, Korea (South) Session G.3: Compressed Sensing Presentation Lecture Track Graphs, Games, Sparsity, and Signal Processing Manuscript Click here to download the manuscript Virtual Presentation Click here to watch in the Virtual Symposium Abstract Orthogonal least squares (OLS) is a classic algorithm for sparse recovery, function approximation, and subset selection. In this paper, we analyze the performance guarantee of the OLS algorithm. Specifically, we show that OLS guarantees the exact reconstruction of any $K$-sparse vector in $K$ iterations, provided that a sensing matrix has unit $\ell_{2}$-norm columns and satisfies the restricted isometry property (RIP) of order $K+1$ with \begin{align*} \delta_{K+1} &

Plan Ahead

## IEEE ISIT 2021

2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia