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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 781790 of 1277 papers

TitleStatusHype
Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method0
Time Complexity Analysis of Evolutionary Algorithms for 2-Hop (1,2)-Minimum Spanning Tree Problem0
Assessing Distribution Network Flexibility via Reliability-based P-Q Area Segmentation0
Differentiable Scaffolding Tree for Molecule Optimization0
Trading Quality for Efficiency of Graph Partitioning: An Inductive Method across Graphs0
Neural Extensions: Training Neural Networks with Set Functions0
Neural Combinatorial Optimization with Reinforcement Learning : Solving theVehicle Routing Problem with Time Windows0
Generative Adversarial Training for Neural Combinatorial Optimization Models0
Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property0
What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization0
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