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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 12511260 of 1277 papers

TitleStatusHype
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective0
Graph Ordering: Towards the Optimal by Learning0
Graph Q-Learning for Combinatorial Optimization0
Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application0
Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective0
GraphThought: Graph Combinatorial Optimization with Thought Generation0
GRASP: Accelerating Shortest Path Attacks via Graph Attention0
Greedy-Based Feature Selection for Efficient LiDAR SLAM0
GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models0
Green Heron Swarm Optimization Algorithm - State-of-the-Art of a New Nature Inspired Discrete Meta-Heuristics0
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