Title

Learning Greedy Algorithms for Maximum Independent Set

Presentation Type

Event

Start Date

27-4-2019 10:50 AM

End Date

27-4-2019 11:29 AM

Abstract

In 2017, Dai, Khalil, Zhang, Dilkina, and Song introduced a machine learning framework for finding greedy algorithm heuristics for many common combinatorial optimization problems. In particular, they implemented their framework to create greedy algorithms for the minimum vertex cover, maximum cut and the traveling salesman problem.

We use this framework to study the problem of finding large independent sets in random regular graphs and random graphs with planted independent sets. We compare the sizes of independent sets found by the learned algorithms to those found by simple heuristics such as (random) GREEDY and MINGREEDY.

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COinS
 
Apr 27th, 10:50 AM Apr 27th, 11:29 AM

Learning Greedy Algorithms for Maximum Independent Set

In 2017, Dai, Khalil, Zhang, Dilkina, and Song introduced a machine learning framework for finding greedy algorithm heuristics for many common combinatorial optimization problems. In particular, they implemented their framework to create greedy algorithms for the minimum vertex cover, maximum cut and the traveling salesman problem.

We use this framework to study the problem of finding large independent sets in random regular graphs and random graphs with planted independent sets. We compare the sizes of independent sets found by the learned algorithms to those found by simple heuristics such as (random) GREEDY and MINGREEDY.