Efficient Euclidean Projections in Linear Time

Abstract

We consider the problem of computing the Euclidean projection of a vector of length $n$ onto a closed convex set including the $\ell_1$ ball and the specialized polyhedra employed in (Shalev-Shwartz & Singer, 2006). These problems have played building block roles in solving several $\ell_1$-norm based sparse learning problems. Existing methods have a worst-case time complexity of $O(n \log n)$. In this paper, we propose to cast both Euclidean projections as root finding problems associated with specific auxiliary functions, which can be solved in linear time via bisection. We further make use of the special structure of the auxiliary functions, and propose an improved bisection algorithm. Empirical studies demonstrate that the proposed algorithms are much more efficient than the competing ones for computing the projections.