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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 201–225 of 1277 papers

TitleStatusHype
An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing—0
GRLinQ: An Intelligent Spectrum Sharing Mechanism for Device-to-Device Communications with Graph Reinforcement Learning—0
Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation for Hierarchical Federated Learning—0
An Unsupervised Learning Framework Combined with Heuristics for the Maximum Minimal Cut ProblemCode0
Decision-Focused Learning to Predict Action Costs for PlanningCode0
Robust Estimation of Regression Models with Potentially Endogenous Outliers via a Modern Optimization Lens—0
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial OptimizationCode0
Cool-Fusion: Fuse Large Language Models without Training—0
Modeling Local Search Metaheuristics Using Markov Decision Processes—0
Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems—0
Take a Step and Reconsider: Sequence Decoding for Self-Improved Neural Combinatorial OptimizationCode1
Enhancing GNNs Performance on Combinatorial Optimization by Recurrent Feature Update—0
Estimating the stability number of a random graph using convolutional neural networksCode0
Generalizing and Unifying Gray-box Combinatorial Optimization Operators—0
PRANCE: Joint Token-Optimization and Structural Channel-Pruning for Adaptive ViT InferenceCode0
VRSD: Rethinking Similarity and Diversity for Retrieval in Large Language Models—0
UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization ProblemsCode2
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems—0
Differentiable Quadratic Optimization For The Maximum Independent Set ProblemCode0
Learning to Remove Cuts in Integer Linear ProgrammingCode0
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling—0
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement LearningCode2
Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial OptimizationCode1
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