Automatic differentiation in robust optimization
Copyright policy )Automatic differentiation in robust optimization
The paper deals with automatic differentiation techniques to avoid the costly finite differencing of robust objective functions and robust constraints. Sensitivities calculated by automatic differentiation are exact and therefore enhance performance. To evaluate the utility of using automatic differentiation in robust optimization, two new robust optimization extensions are developed. One is a sensitivity-based procedure, and the other makes use of experimental design techniques. These two robust optimization extensions are applied to an aircraft sizing test problem.
- University of Notre Dame United States
- Notre Dame University Philippines
experimental design techniques, Sensitivity, stability, parametric optimization, Optimization problems in solid mechanics, sensitivity-based procedure, Other numerical methods in solid mechanics
experimental design techniques, Sensitivity, stability, parametric optimization, Optimization problems in solid mechanics, sensitivity-based procedure, Other numerical methods in solid mechanics
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