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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

Showing 2650 of 1277 papers

TitleStatusHype
CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial OptimizationCode1
Neural Combinatorial Optimization for Real-World RoutingCode1
FusDreamer: Label-efficient Remote Sensing World Model for Multimodal Data ClassificationCode1
Starjob: Dataset for LLM-Driven Job Shop SchedulingCode1
Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop SchedulingCode1
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
Multi Agent Reinforcement Learning for Sequential Satellite Assignment ProblemsCode1
HSEvo: Elevating Automatic Heuristic Design with Diversity-Driven Harmony Search and Genetic Algorithm Using LLMsCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling ProblemsCode1
Beyond the Heatmap: A Rigorous Evaluation of Component Impact in MCTS-Based TSP SolversCode1
Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected AttributesCode1
Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural NetworksCode1
Parallel AutoRegressive Models for Multi-Agent Combinatorial OptimizationCode1
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective PartitioningCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
Take a Step and Reconsider: Sequence Decoding for Self-Improved Neural Combinatorial OptimizationCode1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial OptimizationCode1
GOAL: A Generalist Combinatorial Optimization Agent LearningCode1
Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment ProblemCode1
Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and MoreCode1
Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but ImprovementCode1
RouteExplainer: An Explanation Framework for Vehicle Routing ProblemCode1
MMSR: Symbolic Regression is a Multi-Modal Information Fusion TaskCode1
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