Two frameworks for nonlinear equality constraints in gradient-enhanced local Bayesian optimization achieve deeper convergence with fewer function evaluations than previous constrained BO methods and SciPy/MATLAB quasi-Newton optimizers on unimodal problems with 2-30 variables.
Bayesian Design and Analysis of Computer Experiments: Use of Derivatives in Surface Prediction
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Presents a robust algorithm for learning any coordinate-wise non-decreasing evaluator preference function, with theoretical guarantees that it matches linear performance when linearity holds.
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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Learning What Evaluators Value: A Reliable Approach to Modeling Evaluator Preferences
Presents a robust algorithm for learning any coordinate-wise non-decreasing evaluator preference function, with theoretical guarantees that it matches linear performance when linearity holds.