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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

Showing 151160 of 1277 papers

TitleStatusHype
LIAR: Leveraging Alignment (Best-of-N) to Jailbreak LLMs in Seconds0
CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention0
Scalable iterative pruning of large language and vision models using block coordinate descent0
Approximation Algorithms for Combinatorial Optimization with PredictionsCode0
Large Language Models for Combinatorial Optimization of Design Structure Matrix0
Design And Optimization Of Multi-rendezvous Manoeuvres Based On Reinforcement Learning And Convex Optimization0
Beyond the Heatmap: A Rigorous Evaluation of Component Impact in MCTS-Based TSP SolversCode1
Liner Shipping Network Design with Reinforcement Learning0
MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning0
Neuro-Symbolic Rule Lists0
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