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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 12411250 of 1277 papers

TitleStatusHype
Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems0
GFPack++: Improving 2D Irregular Packing by Learning Gradient Field with Attention0
GNCGCP - Graduated NonConvexity and Graduated Concavity Procedure0
Graph2Seq: Scalable Learning Dynamics for Graphs0
Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art0
Graph Learning: A Survey0
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm0
Graph Minors Meet Machine Learning: the Power of Obstructions0
Graph Neural Networks for Job Shop Scheduling Problems: A Survey0
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