A gradient-enhanced local Bayesian optimization framework that converges optimality as deeply as standard optimizers but with significantly fewer function evaluations on 2-40 dimensional unimodal problems, outperforming them under noisy gradients.
Sliced Gradient-Enhanced Kriging for High-Dimensional Function Approximation
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Bayesian optimization with Gaussian processes unifies minimization, single-point saddle searches, and double-ended path searches on potential energy surfaces through a shared six-step surrogate loop using derivative observations and inverse-distance kernels.
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Efficient Gradient-Enhanced Bayesian Optimizer with Comparisons to Conjugate-Gradient and Quasi-Newton Optimizers for Unconstrained Local Optimization
A gradient-enhanced local Bayesian optimization framework that converges optimality as deeply as standard optimizers but with significantly fewer function evaluations on 2-40 dimensional unimodal problems, outperforming them under noisy gradients.
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A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches
Bayesian optimization with Gaussian processes unifies minimization, single-point saddle searches, and double-ended path searches on potential energy surfaces through a shared six-step surrogate loop using derivative observations and inverse-distance kernels.