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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 1226–1250 of 1277 papers

TitleStatusHype
From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms—0
Fuzzy Integer Linear Programming Mathematical Models for Examination Timetable Problem—0
Gaze-Enabled Egocentric Video Summarization via Constrained Submodular Maximization—0
GenCO: Generating Diverse Designs with Combinatorial Constraints—0
Generalizable Heuristic Generation Through Large Language Models with Meta-Optimization—0
Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems—0
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness—0
Robust Bayesian Inference for Moving Horizon Estimation—0
Generalizing and Unifying Gray-box Combinatorial Optimization Operators—0
Generative Adversarial Training for Neural Combinatorial Optimization Models—0
Generative Neural Network based Spectrum Sharing using Linear Sum Assignment Problems—0
Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning—0
Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver—0
Generic CP-Supported CMSA for Binary Integer Linear Programs—0
Genetic Algorithm Based Combinatorial Optimization for the Optimal Design of Water Distribution Network of Gurudeniya Service Zone, Sri Lanka—0
Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems—0
GFPack++: Improving 2D Irregular Packing by Learning Gradient Field with Attention—0
GNCGCP - Graduated NonConvexity and Graduated Concavity Procedure—0
Graph2Seq: Scalable Learning Dynamics for Graphs—0
Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings—0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art—0
Graph Learning: A Survey—0
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm—0
Graph Minors Meet Machine Learning: the Power of Obstructions—0
Graph Neural Networks for Job Shop Scheduling Problems: A Survey—0
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