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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 461470 of 1277 papers

TitleStatusHype
DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks0
Exploring the Feature Space of TSP Instances Using Quality Diversity0
A Memetic Algorithm Based on Breakout Local Search for the Generalized Travelling Salesman Problem0
Extended Deep Submodular Functions0
Combinatorial Keyword Recommendations for Sponsored Search with Deep Reinforcement Learning0
Fair Disaster Containment via Graph-Cut Problems0
Differentiable Combinatorial Losses through Generalized Gradients of Linear Programs0
A General Large Neighborhood Search Framework for Solving Integer Linear Programs0
Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method0
Focused Jump-and-Repair Constraint Handling for Fixed-Parameter Tractable Graph Problems Closed Under Induced Subgraphs0
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