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Solving the L1 regularized least square problem via a box-constrained smooth minimization

Abstract · Apr 11, 2017 17:41 ·

smooth dual minimization regularized primal solution solver proximal math-oc cs-cv

Arxiv Abstract

  • Majid Mohammadi
  • Wout Hofman
  • Yaohua Tan
  • S. Hamid Mousavi

In this paper, an equivalent smooth minimization for the L1 regularized least square problem is proposed. The proposed problem is a convex box-constrained smooth minimization which allows applying fast optimization methods to find its solution. Further, it is investigated that the property “the dual of dual is primal” holds for the L1 regularized least square problem. A solver for the smooth problem is proposed, and its affinity to the proximal gradient is shown. Finally, the experiments on L1 and total variation regularized problems are performed, and the corresponding results are reported.

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