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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 9911000 of 1277 papers

TitleStatusHype
Assessing and Enhancing Graph Neural Networks for Combinatorial Optimization: Novel Approaches and Application in Maximum Independent Set Problems0
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization0
Assortment Planning with Sponsored Products0
Asteroid Flyby Cycler Trajectory Design Using Deep Neural Networks0
A Survey for Solving Mixed Integer Programming via Machine Learning0
A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization0
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization0
A Survey on Reinforcement Learning for Combinatorial Optimization0
A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy0
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking0
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