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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 3140 of 1277 papers

TitleStatusHype
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time WindowsCode1
Attention, Learn to Solve Routing Problems!Code1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable ApproachCode1
Combinatorial Optimization with Physics-Inspired Graph Neural NetworksCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial OptimizationCode1
Belief Propagation Neural NetworksCode1
A Bayesian algorithm for retrosynthesisCode1
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