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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 181190 of 1277 papers

TitleStatusHype
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
Maximum Entropy Weighted Independent Set Pooling for Graph Neural NetworksCode1
Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time WindowsCode1
ML4CO-KIDA: Knowledge Inheritance in Dataset AggregationCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
Moco: A Learnable Meta Optimizer for Combinatorial OptimizationCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
HSEvo: Elevating Automatic Heuristic Design with Diversity-Driven Harmony Search and Genetic Algorithm Using LLMsCode1
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale GeneralizationCode1
Variational Annealing on Graphs for Combinatorial OptimizationCode1
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