A General Robust-Optimization Formulation for Nonlinear Programming

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dc.contributor.authorZhang, Yin
dc.date.accessioned2018-06-18T17:52:02Z
dc.date.available2018-06-18T17:52:02Z
dc.date.issued2004-07
dc.date.noteJuly 2004 (Revised June 2005)
dc.description.abstractMost 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 app lications where reasonable parameter estimations are available and uncertain var iations are moderate.
dc.format.extent14 pp
dc.identifier.citationZhang, Yin. "A General Robust-Optimization Formulation for Nonlinear Programming." (2004) <a href="https://hdl.handle.net/1911/102025">https://hdl.handle.net/1911/102025</a>.
dc.identifier.digitalTR04-13
dc.identifier.urihttps://hdl.handle.net/1911/102025
dc.language.isoeng
dc.titleA General Robust-Optimization Formulation for Nonlinear Programming
dc.typeTechnical report
dc.type.dcmiText
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