One Shot Optimization with Generalized Constraints

摘要

One-shot optimization is an efficient method for simultaneously solving and optimizing a problem, such as problems in aerodynamic shape optimization. The proposed one-shot framework uses a preconditioned design update in order to ensure convergence of the coupled primal, adjoint, and design solvers. This paper builds upon previous work which applied one-shot optimization to problems with equality constraints on top of the state and co-state equations. In particular, this work proposes a one-shot optimization framework for design problems with inequality constraints.