Ill-Conditioned Convex Processes and Conic Linear Systems

We prove the smallest possible norm of a linear perturbation making a closed convex process nonsurjective is the inverse of the norm of the inverse process. This generalizes the fundamental property of the condition number of a linear map. We then apply this result to strengthen a theorem of Renegar measuring the size of perturbation necessary to make a conic linear system inconsistent.

[1]  Jorge R. Vera Ill-Posedness and the Complexity of Deciding Existence of Solutions to Linear Programs , 1996, SIAM J. Optim..

[2]  James Renegar,et al.  Linear programming, complexity theory and elementary functional analysis , 1995, Math. Program..

[3]  C. Ursescu Multifunctions with convex closed graph , 1975 .

[4]  Jonathan M. Borwein,et al.  Norm duality for convex processes and applications , 1986 .

[5]  James Renegar,et al.  Condition Numbers, the Barrier Method, and the Conjugate-Gradient Method , 1996, SIAM J. Optim..

[6]  Robert M. Freund,et al.  Condition measures and properties of the central trajectory of a linear program , 1998, Math. Program..

[7]  S. M. Robinson Normed convex processes , 1972 .

[8]  Javier Peña,et al.  Understanding the Geometry of Infeasible Perturbations of a Conic Linear System , 1999, SIAM J. Optim..

[9]  James Renegar,et al.  Incorporating Condition Measures into the Complexity Theory of Linear Programming , 1995, SIAM J. Optim..

[10]  Robert M. Freund,et al.  Some characterizations and properties of the “distance to ill-posedness” and the condition measure of a conic linear system , 1999, Math. Program..

[11]  Jorge R. Vera,et al.  On the complexity of linear programming under finite precision arithmetic , 1998, Math. Program..

[12]  Jonathan M. Borwein,et al.  Adjoint Process Duality , 1983, Math. Oper. Res..

[13]  Stephen M. Robinson,et al.  Regularity and Stability for Convex Multivalued Functions , 1976, Math. Oper. Res..