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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 271280 of 1277 papers

TitleStatusHype
Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and MoreCode1
The Curious Case of Class Accuracy Imbalance in LLMs: Post-hoc Debiasing via Nonlinear Integer Programming0
Partial information decomposition: redundancy as information bottleneckCode0
Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization0
FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of Real-World SoCsCode2
Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint0
Federated Combinatorial Multi-Agent Multi-Armed Bandits0
Test-Time Augmentation for Traveling Salesperson ProblemCode0
Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing SolverCode0
Early years of Biased Random-Key Genetic Algorithms: A systematic review0
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