Case Studies for a First-Order Robust Nonlinear Programming Formulation

E. T. Hale () and Y. Zhang
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E. T. Hale: Rice University
Y. Zhang: Rice University

Journal of Optimization Theory and Applications, 2007, vol. 134, issue 1, No 3, 27-45

Abstract: Abstract In this paper, we conduct three case studies to assess the effectiveness of a recently proposed first-order method for robust nonlinear programming [Zhang, Y.: J. Optim. Theory Appl. 132, 111–124 (2007)]. Three robust nonlinear programming problems were chosen from the literature using the criteria that results calculated using other methods must be available and the problems should be realistic, but fairly simple. Our studies show that the first-order method produced reasonable solutions when the level of uncertainty was small to moderate. In addition, we demonstrate a method for leveraging a theoretical result to eliminate constraint violations. Since the first-order method is relatively inexpensive in comparison to other robust optimization techniques, our studies indicate that, under moderate uncertainty, the first-order approach may be more suitable than other methods for large problems.

Keywords: Robust optimization; Nonlinear programming; First-order robustness; Design under uncertainty; Safety constraints (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10957-007-9208-y

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