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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 426–450 of 1277 papers

TitleStatusHype
Using Combinatorial Optimization to Design a High quality LLM Solution—0
Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear ConstraintsCode0
Context-aware Diversity Enhancement for Neural Multi-Objective Combinatorial Optimization—0
The Curious Case of Class Accuracy Imbalance in LLMs: Post-hoc Debiasing via Nonlinear Integer Programming—0
Partial information decomposition: redundancy as information bottleneckCode0
Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization—0
Federated Combinatorial Multi-Agent Multi-Armed Bandits—0
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
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
Message Passing Variational Autoregressive Network for Solving Intractable Ising Models—0
Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective—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
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