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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 6170 of 1277 papers

TitleStatusHype
A Deep Instance Generative Framework for MILP Solvers Under Limited Data AvailabilityCode1
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
DeepACO: Neural-enhanced Ant Systems for Combinatorial OptimizationCode1
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNetsCode1
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNetsCode1
Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local PolicyCode1
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural NetworksCode1
Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approachCode1
Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman ProblemsCode1
JoinGym: An Efficient Query Optimization Environment for Reinforcement LearningCode1
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