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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 3140 of 1277 papers

TitleStatusHype
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
Multi Agent Reinforcement Learning for Sequential Satellite Assignment ProblemsCode1
HSEvo: Elevating Automatic Heuristic Design with Diversity-Driven Harmony Search and Genetic Algorithm Using LLMsCode1
Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling ProblemsCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
Beyond the Heatmap: A Rigorous Evaluation of Component Impact in MCTS-Based TSP SolversCode1
Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected AttributesCode1
Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural NetworksCode1
Parallel AutoRegressive Models for Multi-Agent Combinatorial OptimizationCode1
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective PartitioningCode1
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