Celer: a Fast Solver for the Lasso with Dual Extrapolation

Understand

Convex sparsity-inducing regularizations are ubiquitous in high-dimensional machine learning, but solving the resulting optimization problems can be slow.

• To accelerate solvers, state-of-the-art approaches consist in reducing the size of the optimization problem at hand.
• In the context of regression, this can be achieved either by discarding irrelevant features (screening techniques) or by prioritizing features likely to be included in the support of the solution (working set techniques).
• Duality comes into play at several steps in these techniques.