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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 161170 of 1277 papers

TitleStatusHype
Attention, Learn to Solve Routing Problems!Code1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
Hybrid Pointer Networks for Traveling Salesman Problems OptimizationCode1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approachCode1
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDNCode1
Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial OptimizationCode1
Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop SchedulingCode1
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