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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 911920 of 1277 papers

TitleStatusHype
Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon0
Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation0
Machine Learning Methods for Data Association in Multi-Object Tracking0
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network0
A Simple and Computationally Trivial Estimator for Grouped Fixed Effects Models0
Making Sense Of Distributed Representations With Activation Spectroscopy0
Manipulating Predictions over Discrete Inputs in Machine Teaching0
Mapping Tractography Across Subjects0
Material Identification From Radiographs Without Energy Resolution0
Mathematical Programming Strategies for Solving the Minimum Common String Partition Problem0
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