- A data-independent distance to infeasibility for linear conic systems Javier Pena (jfpandrew.cmu.edu) Vera Roshchina (v.roshchinaunsw.edu.au) Abstract: We offer a unified treatment of distinct measures of well-posedness for homogeneous conic systems. To that end, we introduce a distance to infeasibility based entirely on geometric considerations of the elements defining the conic system. Our approach sheds new light into and connects several well-known condition measures for conic systems, including {\em Renegar's} distance to infeasibility, the {\em Grassmannian} condition measure, a measure of the {\em most interior} solution, as well as the {\em sigma} and {\em symmetry} measures. Keywords: condition number, conic programming, distance to infeasibility, convex duality Category 1: Convex and Nonsmooth Optimization Citation: Working Paper, Carnegie Mellon University, May 2018. Download: [PDF]Entry Submitted: 05/23/2018Entry Accepted: 05/23/2018Entry Last Modified: 05/29/2018Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.