General Robust-Optimization Formulation for Nonlinear Programming
Y. Zhang
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Y. Zhang: Rice University
Journal of Optimization Theory and Applications, 2007, vol. 132, issue 1, No 7, 124 pages
Abstract:
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.
Keywords: Uncertain parameters; nonlinear programming; robust optimization formulations (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/s10957-006-9082-z
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