Optimization Online


Amenable cones: error bounds without constraint qualifications

Bruno F. Lourenco (lourenco***at***mist.i.u-tokyo.ac.jp)

Abstract: We provide a framework for obtaining error bounds for linear conic problems without assuming constraint qualifications or regularity conditions. The key aspects of our approach are the notions of amenable cones and facial residual functions. For amenable cones, it is shown that error bounds can be expressed as a composition of facial residual functions. The number of compositions is related to the facial reduction technique and the singularity degree of the problem. In particular, we show that symmetric cones are amenable and compute facial residual functions. From that, we are able to furnish a new Holderian error bound, thus extending and shedding new light on an earlier result by Sturm on semidefinite matrices. We also provide error bounds for the intersection of amenable cones, this will be used to provided error bounds for the doubly nonnegative cone. At the end, we list some open problems.

Keywords: error bounds, amenable cones, facial reduction, singularity degree, feasibility problem

Category 1: Linear, Cone and Semidefinite Programming

Category 2: Convex and Nonsmooth Optimization


Download: [PDF]

Entry Submitted: 11/27/2017
Entry Accepted: 11/27/2017
Entry Last Modified: 08/09/2019

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society