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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 10711080 of 1277 papers

TitleStatusHype
Scalable Robust Kidney ExchangeCode0
Dynamic Assortment Optimization with Changing Contextual Information0
Differentiable Greedy Networks0
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
Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement LearningCode0
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set MatchingCode0
MRF Optimization with Separable Convex Prior on Partially Ordered Labels0
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