
Convergence of a Piggyback-style method for the differentiation of solutions of standard saddle-point problems
Bogensperger, Lea; Chambolle, Antonin; Pock, Thomas (2022), Convergence of a Piggyback-style method for the differentiation of solutions of standard saddle-point problems. https://basepub.dauphine.psl.eu/handle/123456789/22785
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Type
Document de travail / Working paperExternal document link
https://hal.archives-ouvertes.fr/hal-03516542Date
2022Series title
Cahier de recherche CEREMADE, Université Paris Dauphine-PSLPublished in
Paris
Pages
22
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Show full item recordAuthor(s)
Bogensperger, LeaInstitute for Computer Graphics and Vision [Graz] [ICG]
Chambolle, Antonin

CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Pock, Thomas
Institute for Computer Graphics and Vision [Graz] [ICG]
Abstract (EN)
We analyse a "piggyback"-style method for computing the derivative of a loss which depends on the solution of a convex-concave saddle point problems, with respect to the bilinear term. We attempt to derive guarantees for the algorithm under minimal regularity assumption on the functions. Our final convergence results include possibly nonsmooth objectives. We illustrate the versatility of the proposed piggyback algorithm by learning optimized shearlet transforms, which are a class of popular sparsifying transforms in the field of imaging.Subjects / Keywords
First-order methods; saddle-point problems; differentiation; adjoint methods; piggyback algorithm; learningRelated items
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