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.
A solution to the ill-conditioning of gradient-enhanced covariance matrices for Gaussian processes.International Journal for Numerical Methods in Engineering
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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 Framework for Nonlinearly-Constrained Gradient-Enhanced Local Bayesian Optimization with Comparisons to Quasi-Newton Optimizers
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.
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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.