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

We propose a learning-based approach for the sparse Gaussian Elimination.There are many hard combinatorial optimization problems in modern sparsesolver. These NP-hard problems could be handled in the framework of MarkovDecision Process, especially the Q-Learning technique. We proposed someQ-Learning algorithms for the main modules of sparse solver: minimum degreeordering, task scheduling and adaptive pivoting. Finally, we recast the sparsesolver into the framework of Q-Learning.  Our study is the first step to connect these two classical mathematicalmodels: Gaussian Elimination and Markov Decision Process. Our learning-basedalgorithm could help improve the performance of sparse solver, which has beenverified in some numerical experiments.

Learning the Markov Decision Process in the Sparse Gaussian Elimination