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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 681690 of 1277 papers

TitleStatusHype
Pretrained Cost Model for Distributed Constraint Optimization ProblemsCode0
Decision-Focused Learning: Through the Lens of Learning to RankCode1
Constrained Resource Allocation Problems in Communications: An Information-assisted Approach0
Multidimensional Assignment Problem for multipartite entity resolution0
Learning-based Measurement Scheduling for Loosely-Coupled Cooperative Localization0
Constrained Machine Learning: The Bagel Framework0
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
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming SeedingCode1
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