Partial Rankings of Optimizers
Machine Learning
2024-09-09 v3 Machine Learning
Abstract
We introduce a framework for benchmarking optimizers according to multiple criteria over various test functions. Based on a recently introduced union-free generic depth function for partial orders/rankings, it fully exploits the ordinal information and allows for incomparability. Our method describes the distribution of all partial orders/rankings, avoiding the notorious shortcomings of aggregation. This permits to identify test functions that produce central or outlying rankings of optimizers and to assess the quality of benchmarking suites.
Cite
@article{arxiv.2402.16565,
title = {Partial Rankings of Optimizers},
author = {Julian Rodemann and Hannah Blocher},
journal= {arXiv preprint arXiv:2402.16565},
year = {2024}
}