AI/ML

Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search. (arXiv:1810.10659v1 [cs.LG])


We present a learning-based approach to computing solutions for certain
NP-hard problems. Our approach combines deep learning techniques with useful
algorithmic elements from classic heuristics. The central component is a graph
convolutional network that is trained to estimate the likelihood, for each
vertex in a graph, of whether this vertex is part of the optimal solution. The
network is designed and trained to synthesize a diverse set of solutions, which
enables rapid exploration of the solution space via tree search. The presented
approach is evaluated on four canonical NP-hard problems and five datasets,
which include benchmark satisfiability problems and real social network graphs
with up to a hundred thousand nodes. Experimental results demonstrate that the
presented approach substantially outperforms recent deep learning work, and
performs on par with highly optimized state-of-the-art heuristic solvers for
some NP-hard problems. Experiments indicate that our approach generalizes
across datasets, and scales to graphs that are orders of magnitude larger than
those used during training.

Source link

WordPress database error: [Error writing file '/tmp/MYRkhgZ6' (Errcode: 28 - No space left on device)]
SELECT SQL_CALC_FOUND_ROWS wp_posts.ID FROM wp_posts LEFT JOIN wp_term_relationships ON (wp_posts.ID = wp_term_relationships.object_id) WHERE 1=1 AND wp_posts.ID NOT IN (86942) AND ( wp_term_relationships.term_taxonomy_id IN (313) ) AND wp_posts.post_type = 'post' AND (wp_posts.post_status = 'publish') GROUP BY wp_posts.ID ORDER BY RAND() LIMIT 0, 3

This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Accept Read More

Privacy & Cookies Policy

COVID-19

COVID-19 (Coronavirus) is a new illness that is having a major effect on all businesses globally LIVE COVID-19 STATISTICS FOR World