2 citations · 2 across the 3 of their papers we have counts for
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Scalable Subspace Methods for Derivative-Free Nonlinear Least-Squares Optimization
Coralia Cartis, Lindon Roberts
We introduce a general framework for large-scale model-based derivative-free optimization based on iterative minimization within random subspaces. We present a probabilistic worst-…
Scalable Derivative-Free Optimization for Nonlinear Least-Squares Problems
Coralia Cartis, Tyler Ferguson, Lindon Roberts
Derivative-free - or zeroth-order - optimization (DFO) has gained recent attention for its ability to solve problems in a variety of application areas, including machine learning,…
Inexact Derivative-Free Optimization for Bilevel Learning
Matthias J. Ehrhardt, Lindon Roberts
Variational regularization techniques are dominant in the field of mathematical imaging. A drawback of these techniques is that they are dependent on a number of parameters which h…
Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers
Coralia Cartis, Jan Fiala, Benjamin Marteau +1
We present DFO-LS, a software package for derivative-free optimization (DFO) for nonlinear Least-Squares (LS) problems, with optional bound constraints. Inspired by the Gauss-Newto…
A Derivative-Free Gauss-Newton Method
Coralia Cartis, Lindon Roberts
We present DFO-GN, a derivative-free version of the Gauss-Newton method for solving nonlinear least-squares problems. As is common in derivative-free optimization, DFO-GN uses inte…