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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 501–525 of 1277 papers

TitleStatusHype
From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms—0
CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language Models—0
Entity Summarization: State of the Art and Future Challenges—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
Causal Discovery with Reinforcement Learning—0
Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems—0
An interacting replica approach applied to the traveling salesman problem—0
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness—0
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware—0
Causal Effect Identification in Uncertain Causal Networks—0
Enhancing Robustness of Neural Networks through Fourier Stabilization—0
Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion—0
Can We Learn Heuristics For Graphical Model Inference Using Reinforcement Learning?—0
An Improved Reinforcement Learning Algorithm for Learning to Branch—0
AED: An Anytime Evolutionary DCOP Algorithm—0
Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines—0
Enhancing GNNs Performance on Combinatorial Optimization by Recurrent Feature Update—0
Cakewalk Sampling—0
Enhancing Column Generation by Reinforcement Learning-Based Hyper-Heuristic for Vehicle Routing and Scheduling Problems—0
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization—0
CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention—0
An Improved ACS Algorithm for the Solutions of Larger TSP Problems—0
End-to-end Planning of Fixed Millimeter-Wave Networks—0
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