Quantitative stability of linear infinite inequality systems under block perturbations with applications to convex systems

• M.J. Cánovas ^[1] ; M.A. López ^[2] ; B.S. Mordukhovich ^[3] ; J. Parra ^[1]
  1. [1] Universidad Miguel Hernández de Elche

     Universidad Miguel Hernández de Elche

     Elche, España

     
  2. [2] Universitat d'Alacant

     Universitat d'Alacant

     Alicante, España

     
  3. [3] Wayne State University

     Wayne State University

     City of Detroit, Estados Unidos

     

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• Localización: Top, ISSN-e 1863-8279, ISSN 1134-5764, Vol. 20, Nº. 2, 2012, págs. 310-327
• Idioma: inglés
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• Resumen

  • The original motivation for this paper was to provide an efficient quantitative analysis of convex infinite (or semi-infinite) inequality systems whose decision variables run over general infinite-dimensional (resp. finite-dimensional) Banach spaces and that are indexed by an arbitrary fixed set J. Parameter perturbations on the right-hand side of the inequalities are required to be merely bounded, and thus the natural parameter space is l ∞(J). Our basic strategy consists of linearizing the parameterized convex system via splitting convex inequalities into linear ones by using the Fenchel–Legendre conjugate. This approach yields that arbitrary bounded right-hand side perturbations of the convex system turn on constant-by-blocks perturbations in the linearized system. Based on advanced variational analysis, we derive a precise formula for computing the exact Lipschitzian bound of the feasible solution map of block-perturbed linear systems, which involves only the system’s data, and then show that this exact bound agrees with the coderivative norm of the aforementioned mapping. In this way we extend to the convex setting the results of Cánovas et al. (SIAM J. Optim. 20, 1504–1526, 2009) developed for arbitrary perturbations with no block structure in the linear framework under the boundedness assumption on the system’s coefficients. The latter boundedness assumption is removed in this paper when the decision space is reflexive. The last section provides the aimed application to the convex case.