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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 701–725 of 1277 papers

TitleStatusHype
Reinforcement Learning for Combinatorial Optimization: A Survey—0
Reinforcement Learning for Multi-Truck Vehicle Routing Problems—0
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing—0
Reinforcement Learning to Optimize the Logistics Distribution Routes of Unmanned Aerial Vehicle—0
Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP—0
Reinforcement Learning with Chromatic Networks for Compact Architecture Search—0
Relaxation-assisted reverse annealing on nonnegative/binary matrix factorization—0
RELS-DQN: A Robust and Efficient Local Search Framework for Combinatorial Optimization—0
ReLU Neural Networks of Polynomial Size for Exact Maximum Flow Computation—0
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference—0
Reply to: Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems—0
Reply to: Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set—0
Resolving Overlapping Convex Objects in Silhouette Images by Concavity Analysis and Gaussian Process—0
Rethinking Neural Combinatorial Optimization for Vehicle Routing Problems with Different Constraint Tightness Degrees—0
Rethinking Selection in Generational Genetic Algorithms to Solve Combinatorial Optimization Problems: An Upper Bound-based Parent Selection Strategy for Recombination—0
Revealed Preferences of One-Sided Matching—0
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning—0
Reversible Action Design for Combinatorial Optimization with ReinforcementLearning—0
Revisiting Transformation Invariant Geometric Deep Learning: Are Initial Representations All You Need?—0
RIGA: A Regret-Based Interactive Genetic Algorithm—0
RITFIS: Robust input testing framework for LLMs-based intelligent software—0
Robust Correlation Clustering with Asymmetric Noise—0
Robust Estimation of Regression Models with Potentially Endogenous Outliers via a Modern Optimization Lens—0
An LP-Based Approach for Goal Recognition as Planning—0
Rough matroids based on coverings—0
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