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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 10611070 of 1277 papers

TitleStatusHype
Mixed Uncertainty Sets for Robust Combinatorial Optimization0
Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon0
Scalable Robust Kidney ExchangeCode0
Dynamic Assortment Optimization with Changing Contextual Information0
Differentiable Greedy Networks0
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree SearchCode1
MS-BACO: A new Model Selection algorithm using Binary Ant Colony Optimization for neural complexity and error reduction0
Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume ImagesCode0
Learning to Perform Local Rewriting for Combinatorial OptimizationCode0
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization0
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