Stefan M. Wild

Sequential Learning of Active Subspaces

2021 · arXiv (Cornell University)

In recent years, active subspace methods (ASMs) have become a popular means of performing subspace sensitivity analysis on black-box functions. Naively applied, however, ASMs require gradient evaluations of the target function. In the event of …

Stochastic Trust-Region Algorithm in Random Subspaces with Convergence and Expected Complexity Analyses

2024 · SIAM Journal on Optimization

Here, this work proposes a framework for large-scale stochastic derivative-free optimization (DFO) by introducing STARS, a trust-region method based on iterative minimization in random subspaces. This framework is both an algorithmic and theoretical extension of …