Recent progress in unconstrained nonlinear optimization without derivatives
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Least Frobenius norm updating of quadratic models that satisfy interpolation conditions
Mathematical Programming: Series A and B
Journal of Computational and Applied Mathematics
Adaptation of the UOBYQA algorithm for noisy functions
Proceedings of the 38th conference on Winter simulation
Geometry of interpolation sets in derivative free optimization
Mathematical Programming: Series A and B
Self-Correcting Geometry in Model-Based Algorithms for Derivative-Free Unconstrained Optimization
SIAM Journal on Optimization
SIAM Journal on Optimization
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A numerical study of model-based methods for derivative-free optimization is presented. These methods typically include a geometry phase whose goal is to ensure the adequacy of the interpolation set. The paper studies the performance of an algorithm that dispenses with the geometry phase altogether (and therefore does not attempt to control the position of the interpolation set). Data are presented describing the evolution of the condition number of the interpolation matrix and the accuracy of the gradient estimate. The experiments are performed on smooth unconstrained optimization problems with dimensions ranging between 2 and 15.