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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 326350 of 1277 papers

TitleStatusHype
Learnable Evolutionary Multi-Objective Combinatorial Optimization via Sequence-to-Sequence ModelCode0
Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity AnalysisCode0
Co-training for Policy LearningCode0
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUsCode0
FIS-ONE: Floor Identification System with One Label for Crowdsourced RF SignalsCode0
Futureproof Static Memory PlanningCode0
Lagrange Oscillatory Neural Networks for Constraint Satisfaction and OptimizationCode0
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network PruningCode0
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise ConstraintsCode0
Fair Correlation ClusteringCode0
FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social NetworksCode0
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set MatchingCode0
Exploratory Combinatorial Optimization with Reinforcement LearningCode0
Controlling Continuous Relaxation for Combinatorial OptimizationCode0
Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problemsCode0
Differentiating Through Integer Linear Programs with Quadratic Regularization and Davis-Yin SplittingCode0
Estimating the stability number of a random graph using convolutional neural networksCode0
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search SpacesCode0
Evaluate Quantum Combinatorial Optimization for Distribution Network ReconfigurationCode0
Learning Interpretable Error Functions for Combinatorial Optimization Problem ModelingCode0
Destroy and Repair Using Hyper Graphs for RoutingCode0
Entropy-Guided Sampling of Flat Modes in Discrete SpacesCode0
A random-key GRASP for combinatorial optimizationCode0
EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization FormulationsCode0
Differentiable Model Selection for Ensemble LearningCode0
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