{
  "id": "1409.7193",
  "version": "v1",
  "published": "2014-09-25T09:28:54.000Z",
  "updated": "2014-09-25T09:28:54.000Z",
  "title": "MIST: $l_0$ Sparse Linear Regression with Momentum",
  "authors": [
    "Goran Marjanovic",
    "Magnus O. Ulfarsson",
    "Alfred O. Hero III"
  ],
  "categories": [
    "stat.ML"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-09-25T09:28:54.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "sparse linear regression",
      "large data sets",
      "large linear systems",
      "squares criterion",
      "estimating sparse solutions"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2014arXiv1409.7193M"
    }
  }
}