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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 751760 of 1277 papers

TitleStatusHype
Multidimensional Assignment Problem for multipartite entity resolution0
Learning-based Measurement Scheduling for Loosely-Coupled Cooperative Localization0
Constrained Machine Learning: The Bagel Framework0
Minimizing Polarization and Disagreement in Social Networks via Link Recommendation0
Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Model0
Solving Graph-based Public Goods Games with Tree Search and Imitation LearningCode0
NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs0
Reinforcement Learning Enhanced Explainer for Graph Neural Networks0
Eliciting and Distinguishing Between Weak and Incomplete Preferences: Theory, Experiment and Computation0
Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization0
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