Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression
Deeksha Adil · Richard Peng · Sushant Sachdeva

Wed Dec 11th 05:00 -- 07:00 PM @ East Exhibition Hall B + C #161

Linear regression in Lp-norm is a canonical optimization problem that arises in several applications, including sparse recovery, semi-supervised learning, and signal processing. Generic convex optimization algorithms for solving Lp-regression are slow in practice. Iteratively Reweighted Least Squares (IRLS) is an easy to implement family of algorithms for solving these problems that has been studied for over 50 years. However, these algorithms often diverge for p > 3, and since the work of Osborne (1985), it has been an open problem whether there is an IRLS algorithm that converges for p > 3. We propose p-IRLS, the first IRLS algorithm that provably converges geometrically for any p \in [2,\infty). Our algorithm is simple to implement and is guaranteed to find a high accuracy solution in a sub-linear number of iterations. Our experiments demonstrate that it performs even better than our theoretical bounds, beats the standard Matlab/CVX implementation for solving these problems by 10–50x, and is the fastest among available implementations in the high-accuracy regime.

Author Information

Deeksha Adil (University of Toronto)
Richard Peng (Georgia Tech)
Sushant Sachdeva (University of Toronto)

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