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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 351375 of 1277 papers

TitleStatusHype
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise ConstraintsCode0
Fair Correlation ClusteringCode0
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network PruningCode0
Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problemsCode0
A random-key GRASP for combinatorial optimizationCode0
Exact-K Recommendation via Maximal Clique OptimizationCode0
Estimating the stability number of a random graph using convolutional neural networksCode0
Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear ConstraintsCode0
Evaluate Quantum Combinatorial Optimization for Distribution Network ReconfigurationCode0
Exploratory Combinatorial Optimization with Reinforcement LearningCode0
FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social NetworksCode0
EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization FormulationsCode0
Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy gamesCode0
Enriching Documents with Compact, Representative, Relevant Knowledge GraphsCode0
A GREAT Architecture for Edge-Based Graph Problems Like TSPCode0
Entropy-Guided Sampling of Flat Modes in Discrete SpacesCode0
Efficiently Solve the Max-cut Problem via a Quantum Qubit Rotation AlgorithmCode0
Approximation Algorithms for Combinatorial Optimization with PredictionsCode0
Balancing Utility and Fairness in Submodular Maximization (Technical Report)Code0
Neural Solver Selection for Combinatorial OptimizationCode0
Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language ModelsCode0
DistrictNet: Decision-aware learning for geographical districtingCode0
Differentiable Model Selection for Ensemble LearningCode0
One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint SatisfactionCode0
Dynamic Programming on a Quantum Annealer: Solving the RBC ModelCode0
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