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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 791800 of 1277 papers

TitleStatusHype
On Learning to Solve Cardinality Constrained Combinatorial Optimization in One-Shot: A Re-parameterization Approach via Gumbel-Sinkhorn-TopK0
Learning Scenario Representation for Solving Two-stage Stochastic Integer Programs0
Learning to Solve an Order Fulfillment Problem in Milliseconds with Edge-Feature-Embedded Graph Attention0
An Attention-LSTM Hybrid Model for the Coordinated Routing of Multiple Vehicles0
Preference Conditioned Neural Multi-objective Combinatorial Optimization0
Deep Dynamic Attention Model with Gate Mechanism for Solving Time-dependent Vehicle Routing Problems0
WeaveNet: A Differentiable Solver for Non-linear Assignment Problems0
Differentiable Scaffolding Tree for Molecular Optimization0
Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits0
Target Languages (vs. Inductive Biases) for Learning to Act and Plan0
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