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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 731740 of 1277 papers

TitleStatusHype
Preference Conditioned Neural Multi-objective Combinatorial Optimization0
Trading Quality for Efficiency of Graph Partitioning: An Inductive Method across Graphs0
On Learning to Solve Cardinality Constrained Combinatorial Optimization in One-Shot: A Re-parameterization Approach via Gumbel-Sinkhorn-TopK0
Generative Adversarial Training for Neural Combinatorial Optimization Models0
Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property0
WeaveNet: A Differentiable Solver for Non-linear Assignment Problems0
Neural Extensions: Training Neural Networks with Set Functions0
An Attention-LSTM Hybrid Model for the Coordinated Routing of Multiple Vehicles0
Differentiable Scaffolding Tree for Molecular Optimization0
Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits0
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