Four Hessian-informed trust-region filter variants using low- and high-fidelity surrogates reduce iterations and black-box evaluations by up to an order of magnitude on 25 benchmarks and five engineering cases while lowering tuning sensitivity.
SIAM, Philadelphia (2000)
6 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
A WLaSDI-based framework creates noise-robust latent surrogates for PDE-constrained optimization, deriving direct and adjoint gradients to achieve up to five orders of magnitude speedup on radiative transfer, Vlasov-Poisson, and Burgers benchmarks.
Fully implicit resolvent discretization of noisy accelerated gradient dynamics produces a Lyapunov mean-square recursion whose contraction factor improves and stationary error scales as O(1/α), vanishing for large α under accurate inner solves.
A trust-region funnel algorithm for gray-box optimization achieves global convergence to first-order critical points and performs comparably or better than the classical trust-region filter method.
A sequential convex programming method reformulates non-convex spacecraft pointing objectives into convex cardinality minimization problems to maximize science observation time during a comet flyby under dynamics and fault constraints.
Systematic benchmarks on NACA0012, RAE2822, and ONERA M6 cases show derivative-free optimizers competitive with adjoint-based methods and stronger in higher dimensions.
citing papers explorer
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Trust-region filter algorithms utilizing Hessian information for gray-box optimization
Four Hessian-informed trust-region filter variants using low- and high-fidelity surrogates reduce iterations and black-box evaluations by up to an order of magnitude on 25 benchmarks and five engineering cases while lowering tuning sensitivity.
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Time-Dependent PDE-Constrained Optimization via Weak-Form Latent Dynamics
A WLaSDI-based framework creates noise-robust latent surrogates for PDE-constrained optimization, deriving direct and adjoint gradients to achieve up to five orders of magnitude speedup on radiative transfer, Vlasov-Poisson, and Burgers benchmarks.
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IRON: Implicit Resolvent Optimization under Noise
Fully implicit resolvent discretization of noisy accelerated gradient dynamics produces a Lyapunov mean-square recursion whose contraction factor improves and stationary error scales as O(1/α), vanishing for large α under accurate inner solves.
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A trust-region funnel algorithm for gray-box optimization
A trust-region funnel algorithm for gray-box optimization achieves global convergence to first-order critical points and performs comparably or better than the classical trust-region filter method.
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Optimal Science-time Reorientation Policy for the Comet Interceptor Flyby via Sequential Convex Programming
A sequential convex programming method reformulates non-convex spacecraft pointing objectives into convex cardinality minimization problems to maximize science observation time during a comet flyby under dynamics and fault constraints.
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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.