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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 276–300 of 1277 papers

TitleStatusHype
FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of Real-World SoCsCode2
Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint—0
Test-Time Augmentation for Traveling Salesperson ProblemCode0
Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing SolverCode0
Early years of Biased Random-Key Genetic Algorithms: A systematic review—0
Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization—0
MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-ExpertsCode3
QOPTLib: a Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems—0
On Support Relations Inference and Scene Hierarchy Graph Construction from Point Cloud in Clustered Environments—0
Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Tools—0
Fewer Truncations Improve Language Modeling—0
Proposed modified computational model for the amoeba-inspired combinatorial optimization machine—0
Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning—0
Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective—0
Message Passing Variational Autoregressive Network for Solving Intractable Ising Models—0
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning—0
Mining Potentially Explanatory Patterns via Partial SolutionsCode0
Distributed Task Offloading and Resource Allocation for Latency Minimization in Mobile Edge Computing Networks—0
Deep Reinforcement Learning for Traveling Purchaser Problems—0
Solving the QAP by Two-Stage Graph Pointer Networks and Reinforcement Learning—0
Self-Improved Learning for Scalable Neural Combinatorial Optimization—0
Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but ImprovementCode1
Multi-Robot Connected Fermat Spiral CoverageCode0
Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization—0
Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem—0
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