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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper introduces a new learning-based approach for approximately solvingthe Travelling Salesman Problem on 2D Euclidean graphs. We use deep GraphConvolutional Networks to build efficient TSP graph representations and outputtours in a non-autoregressive manner via highly parallelized beam search. Ourapproach outperforms all recently proposed autoregressive deep learningtechniques in terms of solution quality, inference speed and sample efficiencyfor problem instances of fixed graph sizes. In particular, we reduce theaverage optimality gap from 0.52% to 0.01% for 50 nodes, and from 2.26% to1.39% for 100 nodes. Finally, despite improving upon other learning-basedapproaches for TSP, our approach falls short of standard Operations Researchsolvers.

An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem