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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 831840 of 1277 papers

TitleStatusHype
Towards Constraint-Based Adaptive Hypergraph Learning for Solving Vehicle Routing: An End-to-End Solution0
Towards Decision Support Technology Platform for Modular Systems0
Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization0
Towards Efficient Modularity in Industrial Drying: A Combinatorial Optimization Viewpoint0
Towards fully automated protein structure elucidation with NMR spectroscopy0
Context-aware Diversity Enhancement for Neural Multi-Objective Combinatorial Optimization0
Towards Geometry-Preserving Reductions Between Constraint Satisfaction Problems (and other problems in NP)0
Towards Integrated Glance To Restructuring in Combinatorial Optimization0
Towards Practical Explainability with Cluster Descriptors0
Towards Principled Task Grouping for Multi-Task Learning0
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