Abstract
For perturbed nonlinear programs (NLP) with twice continuously differentiable data, there is a well-developed theory of solution stability based on second-order conditions (SOC), cf., e.g., [2,3,9,11,12]. Motivations for this theory are manifold. For example, convergence analysis of optimization methods, the study of incorrect models, decomposition techniques, semi-infinite programming and input-output modelling lead to the question whether a stationary solution or a local/global minimizer of a NLP behaves stable in some sense. In the following, we give 2nd-order sufficient stability conditions for NLP, allowing some non-smoothness of initial data. The applications mentioned in the title particulary concern iterated local minimization and semi-infinite programming. For brevity of presentation, we refer in this connection only to the recent papers [5] and [6].
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© 1992 Physica-Verlag Heidelberg
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Klatte, D. (1992). Parametric Nonlinear Optimization: Stability of Stationary Solutions and Some Applications. In: Gritzmann, P., Hettich, R., Horst, R., Sachs, E. (eds) Operations Research ’91. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-48417-9_30
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DOI: https://doi.org/10.1007/978-3-642-48417-9_30
Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-0608-3
Online ISBN: 978-3-642-48417-9
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