Definable nonconvex parametric optimization problems admit an adjoint state formula under a qualification condition, selecting a conservative field for the value function without smoothness or uniqueness assumptions.
Snopt: An sqp algorithm for large-scale constrained optimization.SIAM review, 47(1):99–131
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Systematic benchmarks on NACA0012, RAE2822, and ONERA M6 cases show derivative-free optimizers competitive with adjoint-based methods and stronger in higher dimensions.
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The adjoint state method for parametric definable optimization without smoothness or uniqueness
Definable nonconvex parametric optimization problems admit an adjoint state formula under a qualification condition, selecting a conservative field for the value function without smoothness or uniqueness assumptions.
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Derivative-free optimization is competitive for aerodynamic design optimization in moderate dimensions
Systematic benchmarks on NACA0012, RAE2822, and ONERA M6 cases show derivative-free optimizers competitive with adjoint-based methods and stronger in higher dimensions.