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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 721730 of 1277 papers

TitleStatusHype
Time Complexity Analysis of Evolutionary Algorithms for 2-Hop (1,2)-Minimum Spanning Tree Problem0
Hybrid Pointer Networks for Traveling Salesman Problems OptimizationCode1
Assessing Distribution Network Flexibility via Reliability-based P-Q Area Segmentation0
Learning the Markov Decision Process in the Sparse Gaussian EliminationCode1
Learning to Solve an Order Fulfillment Problem in Milliseconds with Edge-Feature-Embedded Graph Attention0
Differentiable Scaffolding Tree for Molecule Optimization0
Learning Scenario Representation for Solving Two-stage Stochastic Integer Programs0
Deep Dynamic Attention Model with Gate Mechanism for Solving Time-dependent Vehicle Routing Problems0
What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization0
Neural Combinatorial Optimization with Reinforcement Learning : Solving theVehicle Routing Problem with Time Windows0
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