Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization

We consider the least squares regression problem, penalized with a combination of the $\ell_{0}$ and $\ell_{2}$ norms (a.k.a. $\ell_0 \ell_2$ regularization). Recent work presents strong evidence that the resulting $\ell_0$-based estimators can outperform popular sparse learning methods, under many important high-dimensional settings. However, exact computation of $\ell_0$-based estimators remains a major challenge. Indeed, state-of-the-art mixed integer programming (MIP) methods for $\ell_0 \ell_2$-regularized regression face difficulties in solving many statistically interesting instances when the number of features $p \sim 10^4$. In this work, we present a new exact MIP framework for $\ell_0\ell_2$-regularized regression that can scale to $p \sim 10^7$, achieving over $3600$x speed-ups compared to the fastest exact methods. Unlike recent work, which relies on modern MIP solvers, we design a specialized nonlinear BnB framework, by critically exploiting the problem structure. A key distinguishing component in our algorithm lies in efficiently solving the node relaxations using specialized first-order methods, based on coordinate descent (CD). Our CD-based method effectively leverages information across the BnB nodes, through using warm starts, active sets, and gradient screening. In addition, we design a novel method for obtaining dual bounds from primal solutions, which certifiably works in high dimensions. Experiments on synthetic and real high-dimensional datasets demonstrate that our method is not only significantly faster than the state of the art, but can also deliver certifiably optimal solutions to statistically challenging instances that cannot be handled with existing methods. We open source the implementation through our toolkit L0BnB.