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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 291–300 of 1277 papers

TitleStatusHype
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning—0
Mining Potentially Explanatory Patterns via Partial SolutionsCode0
Distributed Task Offloading and Resource Allocation for Latency Minimization in Mobile Edge Computing Networks—0
Deep Reinforcement Learning for Traveling Purchaser Problems—0
Solving the QAP by Two-Stage Graph Pointer Networks and Reinforcement Learning—0
Self-Improved Learning for Scalable Neural Combinatorial Optimization—0
Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but ImprovementCode1
Multi-Robot Connected Fermat Spiral CoverageCode0
Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization—0
Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem—0
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