We study high-dimensional sparse linear regression: discover the exact sampling complexity when the data is intrinsically sparse, and establish a sufficient condition on sample size when sparsifying originally dense data.

We propose two scalable DP algorithms for high-dimensional sparse variable selection, leveraging modern mixed-integer programming techniques.