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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 26–50 of 1277 papers

TitleStatusHype
DAG Matters! GFlowNets Enhanced Explainer For Graph Neural NetworksCode1
DeepACO: Neural-enhanced Ant Systems for Combinatorial OptimizationCode1
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph MatchingCode1
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree SearchCode1
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable ApproachCode1
Combinatorial Optimization with Physics-Inspired Graph Neural NetworksCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial OptimizationCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial OptimizationCode1
A Deep Instance Generative Framework for MILP Solvers Under Limited Data AvailabilityCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
CLIPPER: A Graph-Theoretic Framework for Robust Data AssociationCode1
Belief Propagation Neural NetworksCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
A Bayesian algorithm for retrosynthesisCode1
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