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arXiv · 1409.7193

MIST: L0 Sparse Linear Regression with Momentum

Goran Marjanovic·Magnus O. Ulfarsson·Alfred O. Hero III
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Abstract

Significant attention has been given to minimizing a penalized least squares criterion for estimating sparse solutions to large linear systems of equations. The penalty is responsible for inducing sparsity and the natural choice is the so-called $l_0$ norm. In this paper we develop a Momentumized Iterative Shrinkage Thresholding (MIST) algorithm for minimizing the resulting non-convex criterion and prove its convergence to a local minimizer. Simulations on large data sets show superior performance of the proposed method to other methods.

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Goran Marjanovic, Magnus O. Ulfarsson, Alfred O. Hero III. 2014-09-25. MIST: L0 Sparse Linear Regression with Momentum. https://arxiv.org/abs/1409.7193

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