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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 761770 of 1277 papers

TitleStatusHype
Simulating the Effects of Eco-Friendly Transportation Selections for Air Pollution Reduction0
Neural Algorithmic Reasoning for Hypergraphs with Looped Transformers0
Single-Document Summarization as a Tree Knapsack Problem0
Single Document Summarization based on Nested Tree Structure0
SizeGS: Size-aware Compression of 3D Gaussians with Hierarchical Mixed Precision Quantization0
Smooth and Strong: MAP Inference with Linear Convergence0
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form0
Smoothed Online Combinatorial Optimization Using Imperfect Predictions0
SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems0
Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method0
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