Projection-Based Local and Global Lipschitz Moduli of the Optimal Value in Linear Programming

M. J. Cánovas (), M. J. Gisbert (), D. Klatte () and J. Parra ()
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M. J. Cánovas: Miguel Hernández University of Elche
M. J. Gisbert: Universidad Carlos III de Madrid
D. Klatte: Universität Zürich
J. Parra: Miguel Hernández University of Elche

Journal of Optimization Theory and Applications, 2022, vol. 193, issue 1, No 14, 280-299

Abstract: Abstract In this paper, we use a geometrical approach to sharpen a lower bound given in [5] for the Lipschitz modulus of the optimal value of (finite) linear programs under tilt perturbations of the objective function. The key geometrical idea comes from orthogonally projecting general balls on linear subspaces. Our new lower bound provides a computable expression for the exact modulus (as far as it only depends on the nominal data) in two important cases: when the feasible set has extreme points and when we deal with the Euclidean norm. In these two cases, we are able to compute or estimate the global Lipschitz modulus of the optimal value function in different perturbations frameworks.

Keywords: Lipschitz modulus; Optimal value; Orthogonal projections; Linear programming; Variational analysis; 90C31; 49J53; 49K40; 90C05; 15A60 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10957-021-01948-2

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