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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 281–290 of 1277 papers

TitleStatusHype
Early years of Biased Random-Key Genetic Algorithms: A systematic review—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
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