English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Our topic is the use of machine learning to improve software by makingchoices which do not compromise the correctness of the output, but do affectthe time taken to produce such output. We are particularly concerned withcomputer algebra systems (CASs), and in particular, our experiments are forselecting the variable ordering to use when performing a cylindrical algebraicdecomposition of $n$-dimensional real space with respect to the signs of a setof polynomials.  In our prior work we explored the different ML models that could be used, andhow to identify suitable features of the input polynomials. In the presentpaper we both repeat our prior experiments on problems which have morevariables (and thus exponentially more possible orderings), and examine themetric which our ML classifiers targets. The natural metric is computationalruntime, with classifiers trained to pick the ordering which minimises this.However, this leads to the situation were models do not distinguish between anyof the non-optimal orderings, whose runtimes may still vary dramatically. Inthis paper we investigate a modification to the cross-validation algorithms ofthe classifiers so that they do distinguish these cases, leading to improvedresults.

Improved cross-validation for classifiers that make algorithmic choices to minimise runtime without compromising output correctness