Routing (electronic design automation)

Algorithm

Convergence (economics)

Scheduling (production processes)

Operating system

Local search (optimization)

Economics

Component (thermodynamics)

Computer science

Mathematical optimization

Computer network

Thermodynamics

Physics

Reinforcement learning

Parameterized complexity

Mathematics

Artificial intelligence

Rewriting

Programming language

Economic growth

Artificial neural network

Machine learning

Heuristics

Combinatorial optimization

Job shop scheduling

Search-based methods for hard combinatorial optimization are often guided by heuristics. Tuning heuristics in various conditions and situations is often time-consuming. In this paper, we propose NeuRewriter that learns a policy to pick heuristics and rewrite the local components of the current solution to iteratively improve it until convergence. The policy factorizes into a region-picking and a rule-picking component, each parameterized by a neural network trained with actor-critic methods in reinforcement learning. NeuRewriter captures the general structure of combinatorial problems and shows strong performance in three versatile tasks: expression simplification, online job scheduling and vehicle routing problems. NeuRewriter outperforms the expression simplification component in Z3; outperforms DeepRM and Google OR-tools in online job scheduling; and outperforms recent neural baselines and Google OR-tools in vehicle routing problems.

Learning to Perform Local Rewriting for Combinatorial Optimization