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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 51–100 of 1277 papers

TitleStatusHype
Exploring the Power of Graph Neural Networks in Solving Linear Optimization ProblemsCode1
Instance-wise algorithm configuration with graph neural networksCode1
A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing SystemCode1
Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent setCode1
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph MatchingCode1
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective PartitioningCode1
DAG Matters! GFlowNets Enhanced Explainer For Graph Neural NetworksCode1
Large Language Models as Evolutionary OptimizersCode1
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
DeepACO: Neural-enhanced Ant Systems for Combinatorial OptimizationCode1
Feature Importance Ranking for Deep LearningCode1
Equivariant quantum circuits for learning on weighted graphsCode1
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree SearchCode1
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on GraphsCode1
FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot TeamsCode1
GOAL: A Generalist Combinatorial Optimization Agent LearningCode1
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization SolversCode1
Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood SearchCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
Discovering Dynamic Causal Space for DAG Structure LearningCode1
Attention, Learn to Solve Routing Problems!Code1
DOGE-Train: Discrete Optimization on GPU with End-to-end TrainingCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
A Learning-based Iterative Method for Solving Vehicle Routing ProblemsCode1
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
Quantum approximate optimization via learning-based adaptive optimizationCode1
Exact Combinatorial Optimization with Graph Convolutional Neural NetworksCode1
A Deep Instance Generative Framework for MILP Solvers Under Limited Data AvailabilityCode1
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial OptimizationCode1
ASP: Learn a Universal Neural Solver!Code1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
Domain-Independent Dynamic Programming: Generic State Space Search for Combinatorial OptimizationCode1
CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial OptimizationCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time WindowsCode1
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural NetworksCode1
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Active Learning Meets Optimized Item SelectionCode1
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