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




Related posts

`Why not give this work to them?' Explaining AI-Moderated Task-Allocation Outcomes using Negotiation Trees. (arXiv:2002.01640v1 [cs.AI])

Newsemia

Infor to tout FHIR-based data management tech at HIMSS19

Newsemia

BRCA Public Database to Improve Risk Scoring, Cancer Care Decisions

Newsemia

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