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.
Model-Based Derivative-Free Optimization Methods andSoftware
3 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Introduces CLUSTER algorithm extending quadratic-interpolation trust-region methods to handle parameter-change costs, claiming ~50% performance gains on test problems and lab experiments plus an adapted convergence guarantee.
Augments incremental collision laws using the Bouc-Wen model to incorporate external forces as inputs, extends valid parameter ranges, and performs further identification studies on convex viscoplastic body collisions.
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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CLUSTER: Derivative-free optimization of smooth functions with parameter-change costs
Introduces CLUSTER algorithm extending quadratic-interpolation trust-region methods to handle parameter-change costs, claiming ~50% performance gains on test problems and lab experiments plus an adapted convergence guarantee.
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Incremental Collision Laws Based on the Bouc-Wen Model: Improved Collision Models and Further Results
Augments incremental collision laws using the Bouc-Wen model to incorporate external forces as inputs, extends valid parameter ranges, and performs further identification studies on convex viscoplastic body collisions.