Using ℓ1 Regularization to Improve Numerical Partial Differential Equation Solvers

Theresa Scarnati, Anne Gelb, Rodrigo Platte

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Sparse regularization plays a central role in many recent developments in imaging and other related fields. However, it is still of limited use in numerical solvers for partial differential equations (PDEs). In this paper we investigate the use of (Formula presented.) regularization to promote sparsity in the shock locations of hyperbolic PDEs. We develop an algorithm that uses a high order sparsifying transform which enables us to effectively resolve shocks while still maintaining stability. Our method does not require a shock tracking procedure nor any prior information about the number of shock locations. It is efficiently implemented using the alternating direction method of multipliers. We present our results on one and two dimensional examples using both finite difference and spectral methods as underlying PDE solvers.

Original languageEnglish (US)
Pages (from-to)1-28
Number of pages28
JournalJournal of Scientific Computing
StateAccepted/In press - Aug 11 2017


  • $$\ell _1$$ℓ1regularization
  • Alternating direction method of multipliers
  • Image denoising
  • Numerical conservation laws

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Engineering(all)
  • Computational Theory and Mathematics

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