Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning

10 Sep 2018 • Quentin Cappart • Emmanuel Goutierre • David Bergman • Louis-Martin Rousseau

Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems. In the last decade, decision diagrams (DDs) have brought a new perspective on obtaining upper and lower bounds that can be significantly better than classical bounding mechanisms, such as linear relaxations... (read more)

PDF Abstract

Results from the Paper

Add Remove

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

METHOD	TYPE
🤖 No Methods Found	Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet

Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning

Code

Add Remove Mark official

Tasks

Add Remove

Results from the Paper

Add Remove

Methods used in the Paper

Add Remove

Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning

Code Edit Add Remove Mark official

Tasks Edit Add Remove

Results from the Paper Edit Add Remove

Methods used in the Paper Edit Add Remove

Code

Add Remove Mark official

Tasks

Add Remove

Results from the Paper

Add Remove

Methods used in the Paper

Add Remove