Generalized power cones: optimal error bounds and automorphisms

Fuente: arXiv
Guardado en:
Detalles Bibliográficos
Autores principales: Lin, Ying, Lindstrom, Scott B., Lourenço, Bruno F., Pong, Ting Kei
Formato: Preprint
Publicado: 2022
Materias:
Acceso en línea:
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
_version_ 1866911959577788416
author Lin, Ying
Lindstrom, Scott B.
Lourenço, Bruno F.
Pong, Ting Kei
author_facet Lin, Ying
Lindstrom, Scott B.
Lourenço, Bruno F.
Pong, Ting Kei
contents Error bounds are a requisite for trusting or distrusting solutions in an informed way. Until recently, provable error bounds in the absence of constraint qualifications were unattainable for many classes of cones that do not admit projections with known succinct expressions. We build such error bounds for the generalized power cones, using the recently developed framework of one-step facial residual functions. We also show that our error bounds are tight in the sense of that framework. Besides their utility for understanding solution reliability, the error bounds we discover have additional applications to the algebraic structure of the underlying cone, which we describe. In particular we use the error bounds to compute the dimension of the automorphism group for the generalized power cones, and to identify a set of generalized power cones that are self-dual, irreducible, nonhomogeneous, and perfect
format Preprint
id arxiv_https___arxiv_org_abs_2211_16142
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Generalized power cones: optimal error bounds and automorphisms
Lin, Ying
Lindstrom, Scott B.
Lourenço, Bruno F.
Pong, Ting Kei
Optimization and Control
Error bounds are a requisite for trusting or distrusting solutions in an informed way. Until recently, provable error bounds in the absence of constraint qualifications were unattainable for many classes of cones that do not admit projections with known succinct expressions. We build such error bounds for the generalized power cones, using the recently developed framework of one-step facial residual functions. We also show that our error bounds are tight in the sense of that framework. Besides their utility for understanding solution reliability, the error bounds we discover have additional applications to the algebraic structure of the underlying cone, which we describe. In particular we use the error bounds to compute the dimension of the automorphism group for the generalized power cones, and to identify a set of generalized power cones that are self-dual, irreducible, nonhomogeneous, and perfect
title Generalized power cones: optimal error bounds and automorphisms
topic Optimization and Control
url https://arxiv.org/abs/2211.16142