General Robust-Optimization Formulation for Nonlinear Programming |
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Authors: | Y Zhang |
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Institution: | (1) Department of Computational and Applied Mathematics, Rice University, Houston, Texas, USA |
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Abstract: | Most research in robust optimization has been focused so far on inequality-only, convex conic programming with simple linear
models for the uncertain parameters. Many practical optimization problems, however, are nonlinear and nonconvex. Even in linear
programming, the coefficients may still be nonlinear functions of the uncertain parameters. In this paper, we propose robust
formulations that extend the robust-optimization approach to a general nonlinear programming setting with parameter uncertainty
involving both equality and inequality constraints. The proposed robust formulations are valid in a neighborhood of a given
nominal parameter value and are robust to the first-order, thus suitable for applications where reasonable parameter estimations
are available and uncertain variations are moderate.
This work was supported in part by NSF Grant DMS-0405831 |
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Keywords: | Uncertain parameters nonlinear programming robust optimization formulations |
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