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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 1–10 of 1277 papers

TitleStatusHype
LRM-1B: Towards Large Routing Model—0
Large Language Models for Combinatorial Optimization: A Systematic Review—0
Higher-Order Neuromorphic Ising Machines -- Autoencoders and Fowler-Nordheim Annealers are all you need for Scalability—0
On Training-Test (Mis)alignment in Unsupervised Combinatorial Optimization: Observation, Empirical Exploration, and AnalysisCode0
HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization ChallengesCode2
GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models—0
Synthesizing Min-Max Control Barrier Functions For Switched Affine Systems—0
Large Language Models for Design Structure Matrix Optimization—0
Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization—0
Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning EnvironmentCode2
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