Abstract
An algorithm for solving nonlinear programming problems with noisy inequality constraints is developed. The efficiency of the new algorithm relies on the usage of surrogate functions for the unknown exact constraints. These surrogates are based on the bundle idea well-known from nonsmooth optimization and exhibit some very desirable differentiability properties. The overall method is a quasi-Newton type procedure which reduces to a powerful (locally) super-linearly convergent algorithm in the noiseless case provided the bundle is controlled appropriately. It is proved that first order Kuhn-Tucker conditions are satisfied asymptotically. Also a qualification result for the solution obtained by the algorithm is given. Finally, some encouraging numerical tests are reported.
Work supported by the Austrian Academy of Sciences.
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Hintermüller, M. (2000). An Algorithm for Solving Nonlinear Programs with Noisy Inequality Constraints. In: Pillo, G.D., Giannessi, F. (eds) Nonlinear Optimization and Related Topics. Applied Optimization, vol 36. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3226-9_8
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DOI: https://doi.org/10.1007/978-1-4757-3226-9_8
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