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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

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Showing 861–870 of 1277 papers

TitleStatusHype
User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks—0
Using Combinatorial Optimization to Design a High quality LLM Solution—0
Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game—0
Utilizing synchronization to partition power networks into microgrids—0
Explainable quantum regression algorithm with encoded data structure—0
Versatile Black-Box Optimization—0
Virtual Savant: learning for optimization—0
VN-Solver: Vision-based Neural Solver for Combinatorial Optimization over Graphs—0
VRSD: Rethinking Similarity and Diversity for Retrieval in Large Language Models—0
Vulcan: Solving the Steiner Tree Problem with Graph Neural Networks and Deep Reinforcement Learning—0
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