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

Maximum 2-satisfiability (MAX-2-SAT) is a type of combinatorial decisionproblem that is known to be NP-hard. In this paper, we compare LightSolver'squantum-inspired algorithm to a leading deep-learning solver for the MAX-2-SATproblem. Experiments on benchmark data sets show that LightSolver achievessignificantly smaller time-to-optimal-solution compared to a state-of-the-artdeep-learning algorithm, where the gain in performance tends to increase withthe problem size.

Lightsolver challenges a leading deep learning solver for Max-2-SAT problems