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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

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Showing 401–410 of 1277 papers

TitleStatusHype
A new dog learns old tricks: RL finds classic optimization algorithms—0
Decomposed Quadratization: Efficient QUBO Formulation for Learning Bayesian Network—0
A Data-Driven Column Generation Algorithm For Bin Packing Problem in Manufacturing Industry—0
Boosting Ant Colony Optimization via Solution Prediction and Machine Learning—0
Deep Learning of Graph Matching—0
Efficient LDPC Decoding using Physical Computation—0
Efficient learning by implicit exploration in bandit problems with side observations—0
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent—0
Attention Round for Post-Training Quantization—0
Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training—0
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