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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 1120 of 1277 papers

TitleStatusHype
UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization ProblemsCode2
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement LearningCode2
Combinatorial Optimization with Automated Graph Neural NetworksCode2
A Diffusion Model Framework for Unsupervised Neural Combinatorial OptimizationCode2
Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with PerspectivesCode2
FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of Real-World SoCsCode2
Ant Colony Sampling with GFlowNets for Combinatorial OptimizationCode2
Learning to Solve Job Shop Scheduling under UncertaintyCode2
Domain-Independent Dynamic ProgrammingCode2
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial OptimizationCode2
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