Algorithms for Approximate Sparse Regression and Closest Induced Flats

Jean Cardinal, Université Libre de Bruxelles

Date and time: 2pm (New York time), Tuesday, November 30, 2021

Place: On Zoom (details provided on the seminar mailing list)

The sparse regression problem, also known as best subset selection problem, can be cast as follows: Given a set $S$ of n points in $ℝ^d$, a point $y∈ ℝ^d$, and an integer $2 ≤ k ≤ d$, find an affine combination of at most $k$ points of $S$ that is nearest to $y$. We describe a $O(n^{k-1} \mathop{\mathrm{polylog}} n)$-time randomized $(1+ε)$-approximation algorithm for this problem with $d$ and $ε$ constant. This is the first algorithm for this problem running in time $o(n^k)$. Its running time is similar to the query time of a data structure proposed by Har-Peled, Indyk, and Mahabadi, while not requiring any preprocessing. Up to polylogarithmic factors, it matches a conditional lower bound relying on a conjecture about affine degeneracy testing.