Efficient Proximal Mapping Computation for Low-Rank Inducing Norms

Christian Grussler, Pontus Giselsson

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review

Sammanfattning

Low-rank inducing unitarily invariant norms have been introduced to convexify problems with a low-rank/sparsity constraint. The most well-known member of this family is the so-called nuclear norm. To solve optimization problems involving such norms with proximal splitting methods, efficient ways of evaluating the proximal mapping of the low-rank inducing norms are needed. This is known for the nuclear norm, but not for most other members of the low-rank inducing family. This work supplies a framework that reduces the proximal mapping evaluation into a nested binary search, in which each iteration requires the solution of a much simpler problem. The simpler problem can often be solved analytically as demonstrated for the so-called low-rank inducing Frobenius and spectral norms. The framework also allows to compute the proximal mapping of increasing convex functions composed with these norms as well as projections onto their epigraphs.

Originalspråkengelska
Sidor (från-till)168-194
TidskriftJournal of Optimization Theory and Applications
Volym192
Nummer1
Tidigt onlinedatum2021
DOI
StatusPublished - 2022

Ämnesklassifikation (UKÄ)

  • Reglerteknik

Fingeravtryck

Utforska forskningsämnen för ”Efficient Proximal Mapping Computation for Low-Rank Inducing Norms”. Tillsammans bildar de ett unikt fingeravtryck.

Citera det här