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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 981990 of 1277 papers

TitleStatusHype
Multi-objectivization Inspired Metaheuristics for the Sum-of-the-Parts Combinatorial Optimization Problems0
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Multidataset Independent Subspace Analysis with Application to Multimodal FusionCode0
Self-Assignment Flows for Unsupervised Data Labeling on Graphs0
Learning to Order Graph Elements with Application to Multilingual Surface Realization0
Word-level Textual Adversarial Attacking as Combinatorial OptimizationCode0
A Memetic Algorithm Based on Breakout Local Search for the Generalized Travelling Salesman Problem0
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment ProblemCode0
Differentiable Combinatorial Losses through Generalized Gradients of Linear Programs0
Entity Summarization: State of the Art and Future Challenges0
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