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A Penalized Subspace Strategy for Solving Large-Scale Constrained Optimization Problems

Ségolène Martin 1 Emilie Chouzenoux 1 Jean-Christophe Pesquet 2, 1 
1 OPIS - OPtimisation Imagerie et Santé
Inria Saclay - Ile de France, CVN - Centre de vision numérique
Abstract : Many data science problems can be efficiently addressed by minimizing a cost function subject to various constraints. In this paper a new method for solving largescale constrained differentiable optimization problems is proposed. To account efficiently for a wide range of constraints, our approach embeds a subspace algorithm into an exterior penalty framework. The subspace strategy, combined with a Majoration-Minimization step search, takes great advantage of the smoothness of the penalized cost function. Assuming that the latter is convex, the convergence of our algorithm to a solution of the constrained optimization problem is proved. Numerical experiments carried out on a large-scale image restoration application show that the proposed method outperforms stateof-the-art algorithms in terms of computational time.
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Contributor : Emilie Chouzenoux Connect in order to contact the contributor
Submitted on : Thursday, July 1, 2021 - 9:26:12 AM
Last modification on : Tuesday, August 2, 2022 - 3:24:11 AM
Long-term archiving on: : Saturday, October 2, 2021 - 6:28:20 PM


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  • HAL Id : hal-03275387, version 1


Ségolène Martin, Emilie Chouzenoux, Jean-Christophe Pesquet. A Penalized Subspace Strategy for Solving Large-Scale Constrained Optimization Problems. EUSIPCO 2021 - 29th European Signal Processing Conference, Aug 2021, Dublin / Virtual, Ireland. ⟨hal-03275387⟩



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