A General Robust-Optimization Formulation for Nonlinear Programming
Yin Zhang (yzhangcaam.rice.edu)
Abstract: Most research in robust optimization has so far been focused on inequality-only, convex conic programming with simple linear models for uncertain parameters. Many practical optimization problems, however, are nonlinear and non-convex. Even in linear programming, coefficients may still be nonlinear functions of 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 constr aints. 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: Robust optimization, nonlinear programming
Category 1: Robust Optimization
Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )
Citation: Technical Report, TR04-13, Department of Computational and Applied Mathematics, Rice University, Houston, Texas, U.S.A.
Entry Submitted: 06/28/2005
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