Combinatorial Optimization
289 papers with code • 0 benchmarks • 2 datasets
Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.
Benchmarks
These leaderboards are used to track progress in Combinatorial Optimization
Libraries
Use these libraries to find Combinatorial Optimization models and implementations
MemeTracker
CVRPTW
Most implemented papers
Learning the Travelling Salesperson Problem Requires Rethinking Generalization
End-to-end training of neural network solvers for graph combinatorial optimization problems such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently, but remain intractable and inefficient beyond graphs with few hundreds of nodes.
A Reinforcement Learning Environment For Job-Shop Scheduling
Scheduling is a fundamental task occurring in various automated systems applications, e. g., optimal schedules for machines on a job shop allow for a reduction of production costs and waste.
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems
However, the fact is, the state of an instance is changed according to the decision that the model made at different construction steps, and the node features should be updated correspondingly.
Combinatorial Optimization with Physics-Inspired Graph Neural Networks
amazon-research/co-with-gnns-example
•
•
Combinatorial optimization problems are pervasive across science and industry.
Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization
Results from applying the R2 algorithm to instances of a two-dimensional and three-dimensional bin packing problems show that it outperforms generic Monte Carlo tree search, heuristic algorithms and integer programming solvers.
Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume Images
Objective: Deformable image registration is a fundamental problem in medical image analysis, with applications such as longitudinal studies, population modeling, and atlas based image segmentation.
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
maxiboether/mis-benchmark-framework
•
•
NeurIPS 2018
We present a learning-based approach to computing solutions for certain NP-hard problems.
Learning Heuristics over Large Graphs via Deep Reinforcement Learning
Additionally, a case-study on the practical combinatorial problem of Influence Maximization (IM) shows GCOMB is 150 times faster than the specialized IM algorithm IMM with similar quality.
Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets
ricvolpi/domain-shift-robustness
•
•
ICCV 2019
We are concerned with the vulnerability of computer vision models to distributional shifts.
Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero
There have been increasing challenges to solve combinatorial optimization problems by machine learning.
