Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon
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Summary
A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.
- Type
- preprint
- Published
- 2018-11-15
- Cited by
- 1,905
- References
- 75
- Access
- Open access
- OpenAlex
- https://openalex.org/W2900896126
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:53427953
Keywords
Heuristics, Computer science, Machine learning, Artificial intelligence, Point (geometry)
References
- Handbook of Metaheuristics
- The Traveling Salesman Problem: A Computational Study
- Generating new test instances by evolving in instance space
- ASlib: A benchmark library for algorithm selection
- Pattern Recognition and Machine Learning
- Urban Operations Research
- ReACT: Real-Time Algorithm Configuration through Tournaments
- Scientists and the Legacy of World War II: The Case of Operations Research (OR)
- Computability of global solutions to factorable nonconvex programs: Part I — Convex underestimating problems
- Generalized Inverse Multiobjective Optimization with Application to Cancer Therapy
- Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks
- Recurrent policy gradients
- Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research
- Reinforcement Learning: An Introduction
- Learning a synaptic learning rule
- Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools
- DASH: Dynamic Approach for Switching Heuristics
- Learning to Search in Branch and Bound Algorithms
- Mastering the game of Go with deep neural networks and tree search
- A Supervised Machine Learning Approach to Variable Branching in Branch-And-Bound
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- Typed Graph Networks
- PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers
- Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems
- Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems
- REGAL: Transfer Learning For Fast Optimization of Computation Graphs
- Flight-connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer
- TauRieL: Targeting Traveling Salesman Problem with a deep reinforcement learning inspired architecture
- Exact Combinatorial Optimization with Graph Convolutional Neural Networks
- An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
- Tackling Climate Change with Machine Learning
- Optimal Solution Predictions for Mixed Integer Programs
- Online Mixed-Integer Optimization in Milliseconds
- MIPaaL: Mixed Integer Program as a Layer
- Learning to Handle Parameter Perturbations in Combinatorial Optimization: an Application to Facility Location
- Automated quantum programming via reinforcement learning for combinatorial optimization
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