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Combinatorial Optimization

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.

Source: Recent Advances in Neural Program Synthesis

Papers

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Showing 76–100 of 1277 papers

TitleStatusHype
Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood SearchCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Automatic Truss Design with Reinforcement LearningCode1
A Bayesian algorithm for retrosynthesisCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
Belief Propagation Neural NetworksCode1
BILP-Q: Quantum Coalition Structure GenerationCode1
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial OptimizationCode1
CLIPPER: A Graph-Theoretic Framework for Robust Data AssociationCode1
A Deep Instance Generative Framework for MILP Solvers Under Limited Data AvailabilityCode1
Exploring the Power of Graph Neural Networks in Solving Linear Optimization ProblemsCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
A Learning-based Iterative Method for Solving Vehicle Routing ProblemsCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
ASP: Learn a Universal Neural Solver!Code1
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
Active Learning Meets Optimized Item SelectionCode1
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDNCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
DIMES: A Differentiable Meta Solver for Combinatorial Optimization ProblemsCode1
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