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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 341350 of 1277 papers

TitleStatusHype
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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