1 research outputs found
Improved Convergence for and Regression via Iteratively Reweighted Least Squares
The iteratively reweighted least squares method (IRLS) is a popular technique
used in practice for solving regression problems. Various versions of this
method have been proposed, but their theoretical analyses failed to capture the
good practical performance.
In this paper we propose a simple and natural version of IRLS for solving
and regression, which provably converges to a
-approximate solution in
iterations,
where is the number of rows of the input matrix. Interestingly, this
running time is independent of the conditioning of the input, and the dominant
term of the running time depends sublinearly in , which is
atypical for the optimization of non-smooth functions.
This improves upon the more complex algorithms of Chin et al. (ITCS '12), and
Christiano et al. (STOC '11) by a factor of at least , and yields
a truly efficient natural algorithm for the slime mold dynamics
(Straszak-Vishnoi, SODA '16, ITCS '16, ITCS '17).Comment: Appears in ICML 201