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Conference Papers Year : 2021

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

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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Dates and versions

hal-03275387 , version 1 (01-07-2021)

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  • HAL Id : hal-03275387 , version 1

Cite

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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