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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 111120 of 1277 papers

TitleStatusHype
Equivariant quantum circuits for learning on weighted graphsCode1
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDNCode1
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNetsCode1
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNetsCode1
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on GraphsCode1
Exploring the Loss Landscape in Neural Architecture SearchCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
DIMES: A Differentiable Meta Solver for Combinatorial Optimization ProblemsCode1
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
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