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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 791800 of 1277 papers

TitleStatusHype
IA-GM: A Deep Bidirectional Learning Method for Graph Matching0
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization ProblemsCode1
Solve routing problems with a residual edge-graph attention neural networkCode1
Graph Learning: A Survey0
Reconstruction of Convex Polytope Compositions from 3D Point-clouds0
A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble LearningCode0
Exact and Approximate Hierarchical Clustering Using A*0
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
Ecole: A Library for Learning Inside MILP SolversCode0
Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing0
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