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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.<\/jats:p>","DOI":"10.1007\/s10957-021-01948-2","type":"journal-article","created":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T09:07:27Z","timestamp":1634461647000},"page":"280-299","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Projection-Based Local and Global Lipschitz Moduli of the Optimal Value in Linear Programming"],"prefix":"10.1007","volume":"193","author":[{"given":"M. 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