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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 251–260 of 1277 papers

TitleStatusHype
Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization—0
Combinatorial Topic Models using Small-Variance Asymptotics—0
Application of Decision Tree Classifier in Detection of Specific Denial of Service Attacks with Genetic Algorithm Based Feature Selection on NSL-KDD—0
Combining Learned Representations for Combinatorial Optimization—0
Causal Discovery with Reinforcement Learning—0
An interacting replica approach applied to the traveling salesman problem—0
Causal Effect Identification in Uncertain Causal Networks—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
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