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.
Global Optimization of Costly Nonconvex Functions Using Radial Basis Functions
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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.