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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 381390 of 1277 papers

TitleStatusHype
An Efficient Combinatorial Optimization Model Using Learning-to-Rank DistillationCode0
Exploratory Combinatorial Optimization with Reinforcement LearningCode0
Efficiently Solve the Max-cut Problem via a Quantum Qubit Rotation AlgorithmCode0
Efficient Combinatorial Optimization via Heat DiffusionCode0
Graph Neural Networks for the Offline Nanosatellite Task Scheduling ProblemCode0
Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language ModelsCode0
Differentiable Model Selection for Ensemble LearningCode0
Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman ProblemCode0
QAL-BP: An Augmented Lagrangian Quantum Approach for Bin PackingCode0
Ecole: A Library for Learning Inside MILP SolversCode0
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