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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 211220 of 1277 papers

TitleStatusHype
Take a Step and Reconsider: Sequence Decoding for Self-Improved Neural Combinatorial OptimizationCode1
Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems0
Enhancing GNNs Performance on Combinatorial Optimization by Recurrent Feature Update0
Estimating the stability number of a random graph using convolutional neural networksCode0
Generalizing and Unifying Gray-box Combinatorial Optimization Operators0
PRANCE: Joint Token-Optimization and Structural Channel-Pruning for Adaptive ViT InferenceCode0
VRSD: Rethinking Similarity and Diversity for Retrieval in Large Language Models0
UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization ProblemsCode2
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems0
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