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

Cylindrical algebraic decomposition(CAD) is a key tool in computationalalgebraic geometry, particularly for quantifier elimination over real-closedfields. When using CAD, there is often a choice for the ordering placed on thevariables. This can be important, with some problems infeasible with onevariable ordering but easy with another. Machine learning is the process offitting a computer model to a complex function based on properties learned frommeasured data. In this paper we use machine learning (specifically a supportvector machine) to select between heuristics for choosing a variable ordering,outperforming each of the separate heuristics.

Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition