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.
UOBYQA: unconstrained optimization by quadratic approximation
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
This paper isolates admissibility conditions for trust-region radius updates that guarantee first-order stationarity and O(ε^{-2}) complexity, verifies them across five mechanism classes, and extends prior frameworks with new convergence results under linear Hessian growth.
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.
citing papers explorer
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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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A survey of trust-region radius update mechanisms. Part I: First-order analysis
This paper isolates admissibility conditions for trust-region radius updates that guarantee first-order stationarity and O(ε^{-2}) complexity, verifies them across five mechanism classes, and extends prior frameworks with new convergence results under linear Hessian growth.