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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 741750 of 1277 papers

TitleStatusHype
Scalable Relaxations of Sparse Packing Constraints: Optimal Biocontrol in Predator-Prey Network0
Scaling Combinatorial Optimization Neural Improvement Heuristics with Online Search and Adaptation0
ScheduleNet: Learn to Solve MinMax mTSP Using Reinforcement Learning with Delayed Reward0
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning0
Second Order Swarm Intelligence0
Security Defense of Large Scale Networks Under False Data Injection Attacks: An Attack Detection Scheduling Approach0
Segmentation and Optimal Region Selection of Physiological Signals using Deep Neural Networks and Combinatorial Optimization0
Selection of Filters for Photonic Crystal Spectrometer Using Domain-Aware Evolutionary Algorithms0
Self-Assignment Flows for Unsupervised Data Labeling on Graphs0
Self-Evaluation for Job-Shop Scheduling0
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