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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 1011–1020 of 1277 papers

TitleStatusHype
On Support Relations Inference and Scene Hierarchy Graph Construction from Point Cloud in Clustered Environments—0
On the Difficulty of Generalizing Reinforcement Learning Framework for Combinatorial Optimization—0
On the Generalization of Neural Combinatorial Optimization Heuristics—0
On the Mathematical Relationship between Expected n-call@k and the Relevance vs. Diversity Trade-off—0
On the performance of different mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems—0
On the Performance of Metaheuristics: A Different Perspective—0
On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling—0
On the String Kernel Pre-Image Problem with Applications in Drug Discovery—0
Optimal and Fast Real-time Resources Slicing with Deep Dueling Neural Networks—0
Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction—0
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