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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 371380 of 1277 papers

TitleStatusHype
Differentiable Model Selection for Ensemble LearningCode0
EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization FormulationsCode0
On Training-Test (Mis)alignment in Unsupervised Combinatorial Optimization: Observation, Empirical Exploration, and AnalysisCode0
Evaluate Quantum Combinatorial Optimization for Distribution Network ReconfigurationCode0
Efficient Combinatorial Optimization via Heat DiffusionCode0
Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language ModelsCode0
Graph Neural Networks for the Offline Nanosatellite Task Scheduling ProblemCode0
Ecole: A Library for Learning Inside MILP SolversCode0
Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman ProblemCode0
Dynamic Learning of Sequential Choice Bandit Problem under Marketing FatigueCode0
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