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Whitney differentiability of optimal-value functions for bound-constrained convex programming problems. (English) Zbl 07051556
Summary: In the spirit of the Whitney Extension Theorem, consider a function on a compact subset of Euclidean space to be ‘Whitney-differentiable’ if it is a restriction of a continuously Fréchet-differentiable function with an open domain. Whitney-differentiable functions have been shown to have useful (yet possibly nonunique) derivatives and calculus properties even on the boundaries of their domains. This article shows that optimal-value functions for bound-constrained convex programmes with Whitney-differentiable objective functions are themselves Whitney-differentiable, even when the linear-independence constraint qualification is not satisfied. This result extends classic sensitivity results for convex programmes, and generalizes recent work. As an application, sufficient conditions are presented for generating continuously differentiable convex underestimators of nonconvex functions for use in methods for deterministic global optimization in the multivariate McCormick framework. In particular, the main result is applied to generate Whitney-differentiable convex underestimators for quotients of functions with known Whitney-differentiable relaxations.
26B05 Continuity and differentiation questions
90C31 Sensitivity, stability, parametric optimization
90C25 Convex programming
amodMC; ANTIGONE; BARON; dcc; libMC
Full Text: DOI
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