Automatic differentiation in robust optimization

摘要

In this study the use of automatic differentiation techniques significantly reduces the number of analysis calls and the CPU time required for robust optimum design. Traditionally, robust optimum design procedures have relied on finite differencing techniques to evaluate sensitivities for use in gradient-based optimization. Robust objective functions and robust constrains re typically formulated as compound functions that invoke the original objective or constraint many times for each robust evaluation.